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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 NTRS1984

Solar total irradiance and sunspot area in 1981

Precise observations of the total solar irradiance in 1980 from the solar-maximum mission, showed a strong correlation with the projected total sunspot area. This correlation explained about half of the variance (r of about 0.75) in the total-irradiance data, leaving the other half for other solar phenomena and errors in the data. The analysis of the intervening three years of spin-mode data, which have reduced coverage, has begun. It is found that the correlation persists at about the same qualitative level, but with increased scatter that can be attributed to the smaller amount of data. The flatness of the distribution of areas of sunspot groups makes it possible to estimate PSI approximately from only the large groups.

Hudson, H. S.↗

NASA NTRS1995

Studying Solar Irradiance Variability with Wavelet Technique

The detection of variations in solar irradiance by satellite-based experiments during the last 17 years stimulated modelling efforts to help to identify their causes and to provide estimates for irradiance data when no satellite observations exist.

solar irradiance modelling efforst physical origin↗

DOE OSTI2025

Quantifying and simulating the weather forecast uncertainty for advanced building control

Weather forecast uncertainty is unavoidable despite technological advancements. Accurately quantifying and modelling this uncertainty is essential for developing and comparing advanced building controllers. In this study, we present a structured approach using a first-order autoregressive model (AR(1)) to model uncertainty in ambient temperature and global solar irradiation (GHI) forecasts. We analyzed weather data from four cities and employed Jensen–Shannon divergence (JSD) to evaluate the similarity between synthetic and actual forecast errors. The average JSD values for temperature are 0.027 (Berkeley), 0.021 (Leuven), 0.018 (Berlin), and 0.008 (Oslo), and for GHI, the average JSD values are 0.016 (Berkeley), 0.058 (Leuven), and 0.013 (Berlin). The low JSD values indicate a high similarity between the synthetic and real forecast error distributions. Further, our approach successfully generates synthetic weather forecasts that mirror the statistical properties of actual forecasts. The implementation of our method for uncertain forecast generation is being added to the BOPTEST framework.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Observed solar UV irradiance variations of importance to middle atmosphere energetics and photochemistry

Absorption of solar UV irradiance in the spectral interval 120-420 nm is chiefly responsible for radiative heating and photodissociation of important atmospheric constituents (e.g., O2, O3, H2O, NO2, etc.) in the stratosphere, mesosphere, and lower thermosphere. Thus, the absolute value and time perturbations of the UV irradiance could significantly affect the energetics, photochemistry, and subsequent dynamics of these regions. Analysis of preliminary data from the SOLSTICE (UARS) observations for a period of 244 days (3 Oct 1991-2 Jun 1992) is discussed in this paper. The data provide mean daily values of the spectral distribution of the observed irradiances at 1-nm resolution and their solar rotation and semirotation variations. The average amplitudes of the 27-day irradiance oscillations for the 244-day data period were 5.7% at Lyman-alpha (121 nm), 1% at 200 nm, 0.5% at 210 nm, and generally less than 0.2% at wavelengths longer than 280 nm. The average amplitudes of 13.5-day oscillations were, by and large, about half of these values. Solar irradiance variations at 10.7 cm are highly correlated with those at Ly-alpha and other chromospheric emission lines (r = 0.7 to 0.8) and only moderately correlated with irradiances at wavelengths of 180-208 nm (r = 0.5). The correlation decreases as the source region of the irradiance gets closer to the base of the photosphere. At the 2-nm interval 279-281 nm, however, which contains the cores of the Mg II h and k lines, the correlation is again approximately 0.8.

London, Julius↗

DOE OSTI2020

Adversarial super-resolution of climatological wind and solar data

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50 × resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

14 SOLAR ENERGY↗

NASA NTRS1989

Photometric measurements of solar irradiance variations due to sunspots

A photometric telescope constructed to obtain photometric sunspot areas and deficits on a daily basis is described. Data from this Cartesian full disk telescope (CFDT) are analyzed with attention given to the period between June 4 and June 17, 1985 because of the availability of overlapping sunspot area and irradiance deficit data from high-resolution digital spectroheliograms made with the San Fernando Observatory 28 cm vacuum solar telescope and spectroheliograph. The CFDT sunspot deficits suggest a substantial irradiance contribution from faculae and active region plage.

Chapman, G. A.↗

NASA NTRS1985

Analysis of AIS Data of the Recluse Oil Field, Recluse, Wyoming

Airborne Imaging Spectrometer (AIS) data were flown over the Recluse, Wyoming oil field on September 9, 1984. Processing software was developed at Earth Satellite Corporation (EarthSat) for interactive analysis of the AIS data. EarthSat's AIS processing capabilities include destriping, solar irradiance corrections, residual calculations, geometric resampling, equal energy normalization, interactive spectral classifications and a variety of compressive algorithms to reduce the data to 8-bit format with a minimum of information loss. The in-house photolab facilities of EarthSat can routinely produce high-quality color renditions of the enhanced AIS data. A total of 80 lithologic samples were collected under the AIS flight lines. Correlation (within the atmospheric windows) between the laboratory and the AIS spectra of sample sites was generally poor. Reasonable correlation was only possible in large, freshly plowed fields. Mixed pixels and contrast between the natural and sample's surfaces were believed responsible for the poor correlation. Finally, a drift of approximately three channels was observed in the diffraction grating position within the 1.8 to 2.1 micron quadrant.

Dykstra, J. D.↗

NASA NTRS1991

Calibration of EOS multispectral imaging sensors and solar irradiance variability

Earth Observation System (EOS) optical multispectral imaging sensors provide images of the earth at various spectral and spatial resolutions, in the visible (VIS) and infrared (IR) regions of the solar spectrum. Accurate knowledge of extraterrestrial solar spectral irradiance and its variations with time, are needed to trace sensor calibration in space, and for the development of terrestrial atmospheric models needed in data validation. A brief review of the extraterrestrial solar VIS/IR spectral irradiance available in the literature will be reviewed, and the need to develop an extraterrestrial solar spectral irradiance for the EOS studies will be pointed out. The solar calibration of the Earth Radiation Budget Experiments (ERBE), earth-viewing sensors will be discussed. Observed variations in the solar constant (solar irradiance, at the mean sun-earth distance of one astronomical unit, integrated over all wavelengths), and solar spectral irradiance with solar activity and its implications for EOS studies also will be discussed.

Mecherikunnel, Ann↗

NASA NTRS1981

Satellite observed ozone variations and solar activity

The degree of association between total and stratospheric ozone and solar ultraviolet irradiance is evaluated based on mean monthly latitudinally averaged ozone observations from the Nimbus-4 BUV data set. The mean monthly 10.7 cm solar flux f(10.7), adjusted for varying earth-sun distance, is used as a convenient index of solar UV variation. Results show no obvious relationship between total ozone and solar ultraviolet irradiance based on analysis of satellite data over a period of seven years, even though such a relationship has been suggested from theoretical considerations. However, the analysis does indicate that there may be a positive response in the upper tropical stratosphere, at a height of about 45-50 km such that there is an increase in the ozone concentration following an increase in solar UV radiation, which is assumed to be indicated by an increase in f(10.7) flux

London, J.↗

DOE OSTI2022

Solar Radiometer Instrumentation Evaluation: Cooperative Research and Development (Final Report, CRADA Number CRD-16-00619)

The purpose and intent of this agreement is to evaluate newly manufactured thermopile pyranometers and spectroradiometers. The purpose also extends to provide a framework for Participant to conduct research to improve and develop new radiometric devices and application in the future. The overall objective is to provide more accurate, site-specific, long-term, continuous measurements of the solar resources needed by industry to increase the deployment and improve the operations of photovoltaic and concentrating solar power plants. This CRADA addresses the needs for proven solar irradiance measurement to validate resource assessment models and generate high quality data used for site selection, energy system design, deployment, maintenance, and operation. This work will be conducted at NREL and Participant facilities. This project will place instrumentation at the NREL Solar Radiation Research Laboratory (SRRL) in cooperation with EKO Instruments, USA. Participant instruments will be deployed for the purpose of evaluation under controlled conditions. The scope of the project will be a 3 years-long segmented comparison of the instruments vs. other NREL baseline instruments with a well-characterized history. These evaluations will include planned improvements to instruments as well as extensions of future instrument applications. The final evaluation will be a written report similar to the work done by Wilcox and Myers (see http://www.nrel.gov/docs/fy09osti/44627.pdf).

14 SOLAR ENERGY↗

NASA NTRS1998

Validation of 1985-1997 Active Cavity Radiometer Spacecraft Measurements of Total Solar Irradiance Variability

Since 1978, long-term variations in the total solar irradiance (solar constant) have been monitored using spacecraft radiometers, at the 0.01% precision level. The irradiance measurements were performed from the Earth Radiation Budget Satellite [ERBS], Nimbus-7, Solar Maximum Mission [SMM], Upper Atmosphere Research Satellite [UARS], European Retrievable Carrier (EURECA), Solar and Heliospheric Observatory [SOHO], and the Space Shuttle Atmospheric Laboratory for Applications and Science [ATLAS] spacecraft platforms. Radiometer responses can drift or shift at precision levels of a few hundreds of a percent. In-flight calibration sources are not available to detect radiometer response changes at radiometric accuracy or precision levels near the 0.01% (0.1 W/sq m) level. Inconsistent trends among the sets were used to identify possible instrumental drifts or shifts which may be incorrectly interpreted as solar irradiance changes while consistent trends among the different measurement sets were used to detect long-term irradiance variability components. In this paper, 1991-1998 corresponding ERBS, UARS, SOHO, and ATLAS irradiance measurements are inter-compared with each other as well as with the ERBS empirical irradiance fit. The empirical irradiance fit is based upon 10.7-cm solar radio flux (F10) and photometric sunspot index (PSI), indices of solar magnetic activity. Analyses of recent data sets identified no long-term shifts and drifts in the ERBS, SOHO, or UARS data sets. The typical value of the total solar irradiance is approximately 1365 Watts per meter squared (W/sq m).

Lee, Robert B., III↗

NASA NTRS1997

Identification of Solar Cycle 23 Minimum from Solar UV Measurements: NOAA-9 and NOAA-11 SBUV/2, UARS SUSIM, UARS Solstice

The purpose of this report is to present results from the study of solar cycles from solar UV measurements from March 1985 to May 1997. The study determined solar minimum date from daily spectral irradiance data sensitive to noise and the means through which long-term calibration was obtained. In this study magnesium II time series was determined from NOAA-9, and UARS (Upper Atmosphere Research Satellite) SUMIM and SOLSTICE satellites.

DeLand, Matthew T.↗

DOE OSTI2024

Bias Correction and Statistical Downscaling of Solar Radiation Using NA-CORDEX and the NSRDB

The current state-of-art for estimating long-term PV production uses long-term estimates of solar radiation variables, such as global horizontal irradiance (GHI), from previous years. This data is used in models such as the System Advisor Model (SAM) or PYSyst to predict annual production for a PV plant. This information is then used to estimate the production over the next 20 years (a typical plant lifetime) under the assumption that the variability over the current period is representative of the future. As the PV industry moves to extend plant lifetimes to 50 years the current assumptions of representativeness of weather may not be appropriate. This is especially true as our climate changes rapidly. To assess long-term PV production, future projections for solar radiation based on projected carbon emissions are readily available in regional and global climate models. However, climate model projections contain inherent biases that may need to be corrected for accurate analysis of future projections of climate variables. Several studies have analyzed projections of solar radiation for future years, however the accuracy of the model output compared to current and historic data has not been widely studied. Chen (2021) showed that available climate models do not accurately represent solar radiation in some cases, over-projecting GHI at the surface while under-projecting its obstructions, such as clouds and aerosols. This works aims to (1) increase understanding of the accuracy of solar radiation currently available in global and regional climate models and (2) implement bias correction through linear models based on reanalysis data compared to observed solar radiation. The latter aim will be conducted using available observed solar radiation data and modeled data from several regional climate models (RCMs). The bias correction method will be applied to projections of solar radiation resulting in a more accurate representation of the future of solar production.

climate data↗

DOE OSTI2024

The National Climate Database (NCDB): An Unbiased 100-Year Dataset for PV Modeling

In this study, we develop a statistical technique to downscale the future projection of solar irradiance for photovoltaics (PV) energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the downscaling method, a summary of the 100-year dataset from 2001-2100, and future extension of this research.

bias correction↗

NASA NTRS1981

Observational, reference and model data on solar EUV, from measurements on AE-E

Information on solar irradiance at wavelengths below 185 nm, observed by the EUVS experiment on the AE-E satellite over the entire development of the present sunspot cycle 21, is important to a variety of investigations of planetary thermospheres and ionospheres. Since strictly observational information is generally lacking in both the completeness and the spectral detail required by the more advanced programs, it has been necessary to develop computer models in connection with fully detailed compilations of an appropriate reference spectrum. The period of July 13-18, 1976, is selected as an observationally reliable AE-E data reference period reflecting solar conditions of minimum activity for solar cycle 21. It is pointed out that the assignments of absolute irradiance-reference values have been drawn for all available sources of information other than that provided by AE-E.

Hinteregger, H. E.↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

changepoint detection↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗