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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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DOE OSTI2023

pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python’s iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH & ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).

14 SOLAR ENERGY↗

NASA NTRS1988

Sensitivity of satellite-derived net shortwave irradiance at the Earth's surface to radiometric calibration

The effect of radiometric calibration uncertainties on satellite-derived net shortwave irradiance at the Earth's surface was examined. Net shortwave irradiance sensitivity to calibration is expressed as a function of two basic components that depend on surface and cloud albedo sensitivities, respectively. The analysis of these sensitivities for a wide range of atmospheric and surface conditions, as well as radiation geometries, shows that a 10 percent uncertainty in the calibration induces up to 40 W/sqm errors in instantaneous net shortwave irradiance (negative when the calibration uncertainty is positive). The maximum relative errors are obtained in overcast conditions when cloud albedos are high. On a monthly time scale, the induced error becomes typically 13 W/sqm in the tropics and 16 W/sqm in higher latitude regions during summer. The error almost vanishes at high latitudes during winter. A 10 percent positive uncertainty in the calibration gives a net shortwave irradiance error similar to that induced by the 3 hr sampling of the ISCCP Project.

Gautier, C.↗

NASA NTRS1993

Satellite-derived photosynthetically available and total solar irradiance at the surface during FIFE's intensive field campaigns

Satellite-derived photosynthetically available and total solar irradiance at the surface during First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment's (FIFE) intensive field compaigns are addressed. Graphs showing photosynthetically available radiation (PAR) vs. Julian Day and insolation vs. Julian Day are included.

Frouin, Robert↗

NASA NTRS2020

Global and Regional Entropy Production by Radiation Estimated from Satellite Observations

Vertical profiles of shortwave and longwave irradiances computed with satellite-derived cloud properties and temperature and humidity profiles from reanalysis are used to estimate entropy production. Entropy production by shortwave radiation is computed by the absorbed irradiance within layers in the atmosphere and by the surface divided by their temperatures. Similarly, entropy production by longwave radiation is computed by emitted irradiance to space from layers in the atmosphere and surface divided by their temperatures. Global annual mean entropy production by shortwave absorption and longwave emission to space are, respectively, 0.852 and 0.928 W/sq.mK. With a steady-state assumption, entropy production by irreversible processes within the Earth system is estimated to be 0.076 W/sq.mK and by nonradiative irreversible processes to be 0.049 W/sq.mK. Both global annual mean entropy productions by shortwave absorption and longwave emission to space increase with increasing shortwave absorption (i.e., with decreasing the planetary albedo). The increase of entropy production by shortwave absorption is, however, larger than the increase of entropy production by longwave emission to space. The result implies that global annual mean entropy production by irreversible processes decreases with increasing shortwave absorption. Input and output temperatures derived by dividing the absorbed shortwave irradiance and emitted longwave irradiance to space by respective entropy production are, respectively, 282 and 259 K, which give the Carnot efficiency of the Earth system of 8.5%.

Seiji Kato↗

NASA NTRS1992

Greenhouse Effect Detection Experiment (GEDEX). Selected data sets

This CD-ROM contains selected data sets compiled by the participants of the Greenhouse Effect Detection Experiment (GEDEX) workshop on atmospheric temperature. The data sets include surface, upper air, and/or satellite-derived measurements of temperature, solar irradiance, clouds, greenhouse gases, fluxes, albedo, aerosols, ozone, and water vapor, along with Southern Oscillation Indices and Quasi-Biennial Oscillation statistics.

Olsen, Lola M.↗

DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

Xie, Yu↗

DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model: Preprint

DNI↗

DOE OSTI2023

Forecasting Day-Ahead Solar Irradiance for Puerto Rico Using the WRF Model and NSRDB

Accurately predicting solar energy resources is a major challenge in integrating photovoltaics generation on the electric grid. Numerical weather prediction has been recognized by the solar energy community as a major approach to provide solar resource forecasts at various locations and for a variety of timescales. In this study, as a part of the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100), we develop day-head solar irradiance forecast data using the Weather Research and Forecasting (WRF) model at 3 km and hourly/5-minute. The global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts simulated from the WRF model are postprocessed by a simple optimization method using satellite-derived gridded observations from the National Solar Radiation Data Base (NSRDB) to reduce error and bias of the solar irradiance forecasts covering 2018-2020. The NSRDB contributes to improving the GHI and DNI forecasts and also offers the opportunity for an in-depth analysis to evaluate their accuracy over a wide range of Puerto Rico regions. Preliminary results show overall improvements of GHI forecasts up to 37% (DNI: 15%) for mean absolute error and 97% (DNI: 76%) for mean bias error by applying a postprocessing technique to WRF model output.

data models↗

NASA NTRS1990

Comparison of Surface Radiation Budget Satellite algorithms for downwelled shortwave irradiance with Wisconsin Fire/SRB surface-truth data

Surface radiation instruments were operated at various locations during the Wisconsin First ISSCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) and Surface Radiation Budget (SRB) experiment in October 1986. Satellite data were distributed to scientists who had previously developed satellite algorithms to estimate downwelled shortwave irradiance. Results of intercomparison of ground-truth values with the satellite-derived estimates are described.

Whitlock, C. H.↗

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↗

DOE OSTI2024

The National Climate Data Base (NCDB): A Bias-Corrected High-Resolution Climate Dataset

Assessing renewable energy resources under future climate scenarios has been highlighted in recent years to analyze and understand potential impacts of future change in renewable generation on the power sector. Solar energy is well-known as the most plentiful among various renewable resources and usually converted to electricity using photovoltaics (PV) technologies, and the global deployment of PV technology has increased rapidly in recent decades. In this study, we develop a statistical technique to downscale the future projection of solar irradiance for 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 proposed downscaling method and future extension of this research.

climate data↗

NASA NTRS1995

A versatile system for processing geostationary satellite data with run-time visualization capability

To better predict global climate change, scientists are developing climate models that require interdisciplinary and collaborative efforts in their building. We are currently involved in several such projects but will briefly discuss activities in support of two such complementary projects: the Atmospheric Radiation Measurement (ARM) program of the Department of Energy and Sequoia 2000, a joint venture of the University of California, the private sector, and government agencies. Our contribution to the ARM program is to investigate the role of clouds on the top of the atmosphere and on surface radiance fields through the data analysis of surface and satellite observations and complex modeling of the interaction of radiation with clouds. One of our first ARM research activities involves the computation of the broadband shortwave surface irradiance from satellite observations. Geostationary satellite images centered over the first ARM observation site are received hourly over the Internet network and processed in real time to compute hourly and daily composite shortwave irradiance fields. The images and the results are transferred via a high-speed network to the Sequoia 2000 storage facility in Berkeley, where they are archived These satellite-derived results are compared with the surface observations to evaluate the accuracy of the satellite estimate and the spatial representation of the surface observations. In developing the software involved in calculating the surface shortwave irradiance, we have produced an environment whereby we can easily modify and monitor the data processing as required. Through the principles of modular programming, we have developed software that is easily modified as new algorithms for computation are developed or input data availability changes. In addition, the software was designed so that it could be run from an interactive, icon-driven, graphical interface, TCL-TK, developed by Sequoia 2000 participants. In this way, the data flow can be interactively assessed and altered as needed. In this environment, the intermediate data processing 'images' can be viewed, enabling the investigator to easily monitor the various data processing steps as they progress. Additionally, this environment allows the rapid testing of new processing modules and allows their effects to be visually compared with previous results.

Landsfeld, M.↗

NASA NTRS2018

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches.

Bio-optics↗

DOE OSTI2023

Using High-Resolution NSRDB Data to Evaluate Cloud Mask Forecast from WRF-Solar EPS

Validating spatiotemporal distributions of cloud forecasts using numerical weather prediction (NWP) models is difficult as this requires high-quality cloud-property at significantly high spatial and temporal resolution over extended periods of time. Observations of cloud properties, such as cloud mask, cloud optical thickness, and cloud type, are vital for assessing the capability of NWP models to forecast various types of clouds. Using the National Solar Radiation Database (NSRDB), this research evaluates ensemble cloud-mask predictions from the WRF-Solar ensemble prediction system (WRF-Solar EPS). From the WRF-Solar EPS, day-ahead solar forecasts for the contiguous United States (CONUS) for 2018 are simulated. Given the NSRDB data is accessible at a resolution of 2 km, we can calculate the cloud fraction across the 9-km grid of WRF-Solar EPS. This allows us to spatially assess the cloud-mask forecasts using two methods against the high-resolution NSRDB: (a) considering all 2-km NSRDB clouds in the forecast domain (EMAll), and (b) using a minimum cloud fraction threshold of 50% to designate a pixel as cloudy (EMP50). The low-resolution cloud masks from WRF-Solar EPS are evaluated directly against the cloud-resolving scale gridded observations from NSRDB using EMAll. With EMP50, we presume that scenes with less than 50% cloud cover from the 2-km NSRDB are clear. Thus, this assessment approach allows for a fair comparison with WRF-Solar EPS resolved to a 9-km grid. A method of point-by-point verification is used to evaluate dichotomous (yes/no) cloud mask predictions against the NSRDB. For each pixel of model extent, cloud frequency and traditional metrics (e.g., probability of detection, false alarm rate, and hit rate, etc.) are computed and compared with satellite-derived data sets. Mismatched cloud frequency (MCF) is computed to measure the present capability of WRF-Solar EPS in representing various types of clouds, which are categorized using three levels of cloud top height (CTH) and cloud optical depth (COD) across entire CONUS. Preliminary results show that the WRF-Solar EPS provides MCF values ranging from 9% to 46%, 16% to 33%, and 8% to 27% for low-level, middle-level, and high-level clouds, respectively, for three CTHs. The model produces MCFs ranging from 27% to 46%, 13% to 34%, and 8% to 19% for thin, medium-thickness, and thick clouds, respectively, for three CODs. The presentation will include a detailed description of the current outcomes as well as potential future extensions. The evaluation approach established in this study is readily extensible to the evaluation of cloud predictions from different ensemble NWP models. In addition, the findings of the suggested evaluation technique aid in identifying model weaknesses and will ultimately lead to advances in WRF-Solar EPS's skill in predicting clouds and solar irradiance.

cloud mask forecast↗

DOE OSTI2024

The influence of cloud cover on the reliability of satellite-based solar resource data

Satellite-based solar resource data are often developed and validated by using binary cloudiness categories: clear sky or overcast cloudy sky. To investigate the reliability of solar resource data in partially cloudy conditions, we estimate cloud fraction using two distinct algorithms: a physical retrieval model using surface observed global horizontal irradiance (GHI) and direct normal irradiance (DNI) and a temporal average of cloud mask data estimated by the observed DNI. Our analysis reveals a significant presence of scattered clouds, broken clouds, and mismatches between satellite- and surface-based cloud data at 17 surface sites across the contiguous United States, though confidently clear and cloudy conditions collectively account for more than 70 % of the data. Solar radiation is computed using the National Solar Radiation Database (NSRDB) algorithm and validated using surface observations. Here, our findings suggest that, in the presence of scattered clouds, NSRDB data for clear-sky conditions can be subject to significant overestimation. In cloudy-sky conditions classified by satellite data, DNI computed by the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) can be underestimated when limited clouds are detected by surface observations. The bias observed in several cloudiness categories indicates that the NSRDB is exceptionally accurate in confidently clear conditions. However, clear-sky conditions with scattered clouds and mismatched cloud data contribute significantly to the overall uncertainties in the NSRDB. Therefore, future improvements in solar resource data should involve development and implementation of satellite-derived cloud fraction and should consider a novel radiative transfer model accounting for amplified cloud reflection. The evaluation within cloudiness categories also provides a physical rationale for the superior performance of FARMS-DNI compared to the Direct Insolation Simulation Code (DISC) in both cloudy-sky and all-sky conditions.

14 SOLAR ENERGY↗

NASA NTRS1990

Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.

Banse, Karl↗

NASA NTRS2000

OCTS And Seawifs Bio-Optical Algorithm and Product Vaildattion and Intercomparison in US Coastal Waters

Monitoring the health of U.S. coastal waters is an important goal of the National Oceanic and Atmospheric Administration (NOAA). Satellite sensors are capable of providing daily synoptic data of large expanses of the U.S. coast. Ocean color sensor, in particular, can be used to monitor the water quality of coastal waters on an operational basis. To appraise the validity of satellite-derived measurements, such as chlorophyll concentration, the bio-optical algorithms used to derive them must be evaluated in coastal environments. Towards this purpose, over 21 cruises in diverse U.S. coastal waters have been conducted. Of these 21 cruises, 12 have been performed in conjunction with and under the auspices of the NASA/SIMBIOS Project. The primary goal of these cruises has been to obtain in-situ measurements of downwelling irradiance, upwelling radiance, and chlorophyll concentrations in order to evaluate bio-optical algorithms that estimate chlorophyll concentration. In this Technical Memorandum, we evaluate the ability of five bio-optical algorithms, including the current SeaWiFS algorithm, to estimate chlorophyll concentration in surface waters of the South Atlantic Bight (SAB). The SAB consists of a variety of environments including coastal and continental shelf regimes, Gulf Stream waters, and the Sargasso Sea. The biological and optical characteristics of the region is complicated by temporal and spatial variability in phytoplankton composition, primary productivity, and the concentrations of colored dissolved organic matter (CDOM) and suspended sediment. As such, the SAB is an ideal location to test the robustness of algorithms for coastal use.

Brow, Chirstopher↗

NASA NTRS2001

Sun and Sky Radiance Measurements and Data Analysis Protocols

This chapter is concerned with two types of radiometric measurements essential to verify atmospheric correction algorithms and to calibrate vicariously satellite ocean color sensors. The first type is a photometric measurement of the direct solar beam to determine the optical thickness of the atmosphere. The intensity of the solar beam can be measured directly, or obtained indirectly from measurements of diffuse global upper hemispheric irradiance. The second type is a measurement of the solar aureole and sky radiance distribution using a CCD camera, or a scanning radiometer viewing in and perpendicular to the solar principal plane. From the two types of measurements, the optical properties of aerosols, highly variable in space and time, can be derived. Because of the high variability, the aerosol properties should be known at the time of satellite overpass. Atmospheric optics measurements, however, are not easy to perform at sea, from a ship or any platform. This complicates the measurement protocols and data analysis. Some instrumentation cannot be deployed at sea, and is limited to island and coastal sites. In the following, measurement protocols are described for radiometers commonly used to measure direct atmospheric transmittance and sky radiance, namely standard sun photometers, fast-rotating shadow-band radiometers, automated sky scanning systems, and CCD cameras. Methods and procedures to analyze and quality control the data are discussed, as well as proper measurement strategies for evaluation of atmospheric correction algorithms and satellite-derived ocean color.

Frouin, Robert↗