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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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16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Monthly Mean DNI and GTI Derived from Monthly Mean GHI and DHI Using Two Methods: Comparisons with the BSRN Data and the Results Derived from the CERES Hourly Data

Monthly mean Global Horizontal Irradiances (GHI) and Diffuse Horizontal Irradiances (DHI) are more widely available than monthly mean Direct Normal Irradiances (DNI) and Global Tilted Irradiances (GTI). Empirical methods have been developed to derive monthly mean DNI and GTI from monthly mean GHI or from GHI and DHI. In this paper, we evaluate two such methods. The first one was the Whitlock Method developed by Charles H. Whitlock (2005) for the NASA POWER project by means of regression of the BSRN data. The method expresses the monthly mean DHI-to-GHI ratio as polynomial functions of monthly mean clearness index, sunset hour angle and noon solar elevation angle on the monthly-average-day. The monthly mean DNI is calculated by dividing the monthly mean GHI-DHI difference, or DirHI, by the cosine of the solar zenith angle at the mid-time between sunrise and solar noon on the monthly-average-day. The second method is the LJCR Method developed by Liu and Jorden (1960) and Collares-Pereira and Rabl (1979), and this method empirically splits monthly mean GHI and DHI into hourly means on the monthly-average-day, and the resulting hourly mean GHI and DHI and their difference, DirHI, can then be used to compute the monthly mean DNI, GTI and the global solar tracker irradiance (GTrI). This method is also used by RETScreen. We recently produced a set of hourly DNI and DHI by bias-correcting the CERES hourly DNI and DHI, and computed hourly GTI and GTrI as well. The data span twenty plus years from March 2000 to near present on a 1 by 1 grid system. The monthly mean CERES GHI and the corrected DHI are used as inputs to the above two methods to compute monthly mean DNI, GTI and GTrI. Through comparisons with the BSRN data, it is found that the Whitlock Method, with slight modification, and the LJCR Method can produce results that are nearly as good as the results derived from the CERES hourly data.

Taiping Zhang↗
NASA NTRSDate not supplied

The Use of the BSRN Data as A Benchmark for the POWER Hourly DHI and DNI and In Validating Derived Hourly GTI

The satellite-based CERES SYN1deg hourly data is the source data of the POWER GIS solar data that covers 2001 to near present. The SYN1deg(Ed4.1) hourly GHI agrees well with the BSRN data, but the hourly DHI and DirHI (Direct Horizontal Irradiance) are positively and negatively, respectively, biased with appreciable magnitudes. The hourly DNI, derived by dividing the DirHI by cos(SZA), or the cosine of the solar zenith angle, is therefore negatively biased. Based on the statistics of comparisons with the BSRN data, we performed bias corrections on the hourly DHI and DNI. The corrections were executed in the 3-D phase space of latitude, cos(SZA), and cloud fraction (CLFR). The isotropic model is then used to derive the hourly global tilted irradiance (GTI). For validation purpose, we applied the isotropic model to the BSRN data at the original 1-, 2-, 3- or 5-minute interval. The satellite-based hourly GTI shows good agreement with their BSRN counterpart. We also examined two monthly-mean-based methods that empirically derive monthly mean GTI and DNI from monthly mean GHI and from both monthly mean GHI and DHI. The monthly-mean-based results compare favorably with the hourly-mean-based results. The GEWEX SRB (V4-IP) provides POWER with daily mean GHI for the years before the CERES era, and the data were corrected using quantile mapping by referencing the CERES SYN1deg data. We used the Kolmogorov -Smirnov test (K-S test) and Cramer-von Mises test to examine how well the results agree with the BSRN data. We found that if we set the lower limit for the daily mean GHI to 30 W m-2, the data can pass the K-S test at 0.01 significance level and the Cramer-von Mises test at 0.001 significance level. If no lower limit is set on the daily means, the data fail both tests. The satellite-based CERES SYN1deg hourly data is the source data of the POWER GIS solar data that covers 2001 to near present. The SYN1deg(Ed4.1) hourly GHI agrees well with the BSRN data, but the hourly DHI and DirHI (Direct Horizontal Irradiance) are positively and negatively, respectively, biased with appreciable magnitudes. The hourly DNI, derived by dividing the DirHI by cos(SZA), or the cosine of the solar zenith angle, is therefore negatively biased. Based on the statistics of comparisons with the BSRN data, we performed bias corrections on the hourly DHI and DNI. The corrections were executed in the 3-D phase space of latitude, cos(SZA), and cloud fraction (CLFR). The isotropic model is then used to derive the hourly global tilted irradiance (GTI). For validation purpose, we applied the isotropic model to the BSRN data at the original 1-, 2-, 3- or 5-minute interval. The satellite-based hourly GTI shows good agreement with their BSRN counterpart. We also examined two monthly-mean-based methods that empirically derive monthly mean GTI and DNI from monthly mean GHI and from both monthly mean GHI and DHI. The monthly-mean-based results compare favorably with the hourly-mean-based results. The GEWEX SRB (V4-IP) provides POWER with daily mean GHI for the years before the CERES era, and the data were corrected using quantile mapping by referencing the CERES SYN1deg data. We used the Kolmogorov -Smirnov test (K-S test) and Cramer-von Mises test to examine how well the results agree with the BSRN data. We found that if we set the lower limit for the daily mean GHI to 30 W m-2, the data can pass the K-S test at 0.01 significance level and the Cramer-von Mises test at 0.001 significance level. If no lower limit is set on the daily means, the data fail both tests.

Taiping Zhang↗
NASA NTRSDate not supplied

The Hourly GHI, DHI and DNI: Intercomparison of the CERES-Based Data and the NSRDB Data through Comparison with the Ground-Based BSRN Data

The NASA Clouds and the Earth’s Radiant Energy System (CERES) SYN1deg(Ed4.1) provides hourly global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct horizontal irradiance (DirHI) at 1-degree latitude by 1-degree longitude resolution, and the time span is from March 2000 to near present. While the GHI agrees well with the ground-based Baseline Surface Radiation Network (BSRN) data, the DHI and DirHI are, respectively, positively and negatively biased against the BSRN data, and so is the direct normal irradiance (DNI) derived simply by dividing the DirHI by the cosine of the hourly solar zenith angle (SZA). We found that the biases tend to be well-defined functions of cos(SZA) and the cloud fraction (CLFR). We thus performed a bias-correction on the hourly DHI and DNI in the latitude-cos(SZA)-CLFR phase space, and therefrom, we derived the hourly global tilted irradiance (GTI) at a number of tilt angles using the isotropic diffuse irradiance model, and the results agree well with the GTI derived from the original BSRN near-instantaneous records. Meanwhile, the National Renewable Energy Laboratory (NREL) has produced the National Solar Radiation Database (NSRDB) with finer spatiotemporal resolution using its Physical Solar Model (PSM) and surface-based measured data, model results and satellite-based data as inputs. Now the NSRDB data covers not only the United States and its neighboring regions, but Europe, Africa, Asia and Oceania. The available temporal resolutions are 5, 10, 15, 30 and 60 minutes and the spatial resolutions are 2, 4 and 10 km, depending on the selected region. In this presentation, we compare both the CERES-based and NSRDB GHI, DHI and DNI with their BSRN counterparts and show how finer spatial resolution may give us an advantage in data accuracy and usability.

Taiping Zhang↗
DOE OSTI2026

Worldwide benchmarking of cost-effective radiometers for direct and diffuse irradiance

Solar energy projects can benefit from direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) measurements during all project phases. Several commercial measurement systems for DNI and DHI are available. Sun trackers with pyranometers and pyrheliometers can provide highly accurate measurements but are often impractical in solar energy applications. For less expensive and more robust sensors, it is often unclear which accuracy can be expected under a project site's specific atmospheric conditions. We address this challenge through our dedicated experimental comparison of relevant sensor systems (rotating shadowband irradiometer [short RSI], Delta-T SPN1, EKO MS-90, PyranoCam, Sunto CaptPro, Kipp & Zonen CSD3) at up to six sites worldwide. The RSI systems (rRMSD 3 to 8.6%, DNI; 4.8 to 7.6%, DHI) and PyranoCam (rRMSD 2.6 to 5.2%, DNI; 4.4 to 5.8%, DHI) exhibit similar error metrics and are the most accurate systems in the test. Delta-T SPN1 and EKO MS-90 (rRMSD 6.8 to 15%, DNI; 10.6 to 20.1%, DHI) but especially Kipp & Zonen CSD3 and Sunto CaptPro show significant deviations (rRMSD 17.7 to 20%, DNI; 33 to 58%, DHI). We evaluate the influence of relevant atmospheric parameters on the sensors' accuracies by a rather unique measurement setup. MS-90's DNI errors depend on DNI itself, with overestimations for low reference DNI. The deviations of SPN1's DHI and DNI measurements increase sharply in situations with high circumsolar irradiance. Also CaptPro and CSD3's increased measurement errors are related to circumsolar irradiance. For RSI and PyranoCam, only moderate influences on the measurements are identified, indicating a general applicability of these instruments.

14 SOLAR ENERGY↗
NASA NTRS2024

A Break In the Size-Stellar Mass Relation: Evidence for Quenching and Feedback in Dwarf Galaxies

Mapping stars and gas in nearby galaxies is fundamental for understanding their growth and the impact of their environment. This issue is addressed by comparing the stellar ‘edges’ of galaxies D stellar, defined as the outermost diameter where in situ star formation significantly drops, with the gaseous distribution parameterized by the neutral atomic hydrogen diameter measured at 1M⊙/pc2,DHI. By sampling a broad HI mass range 105M⊙< MHI<1011M⊙, we find several dwarf galaxies withMHI<109M⊙from the field and Fornax Cluster which are distinguished by D stellar>> DHI. For the cluster dwarfs, the average HI surface density near D stellar is∼0.3M⊙/pc2, reflecting the impact of quenching and outside-in gas removal from ram pressure and tidal interactions. In comparison, D stellar/DHI ranges between 0.5–2 in dwarf field galaxies, consistent with the expectations from stellar feedback. Only more massive disk galaxies in the field can thus be characterized by the common assumption that D stellar≲DHI. We discover a break in the D stellar−M⋆relation atm break∼4×108M⊙that potentially differentiates the low mass regime where the influence of stellar feedback and environmental processes more prominently regulates the sizes of nearby galaxies. Our results highlight the importance of combining deep optical and HI imaging for understanding galaxy evolution.

Scaling relations↗
NASA NTRS2002

Measuring Pressure Has a New Standard

The Force-Balanced Piston Gauge (FPG) tests and calibrates instrumentation operating in the low pressure range. The system provides a traceable, primary calibration standard for measuring pressures in the range of near 0 to 15 kPa (2.2 psi) in both gauge and absolute measurement modes. The hardware combines a large area piston-cylinder with a load cell measuring the force resulting from pressures across the piston. The mass of the piston can be tared out, allowing measurement to start from zero. A pressure higher than the measured pressure, which keeps the piston centered, lubricates an innovative conical gap located between the piston and the cylinder, eliminating the need for piston rotation. A pressure controller based on the control of low gas flow automates the pressure control. DHI markets the FPG as an automated primary standard for very low-gauge and absolute pressures. DHI is selling the FPG to high-end metrology laboratories on a case by case basis, with a full commercial release to follow.

Source record↗
DOE OSTI2024

National Climate Database (NCDB)

The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.

Array↗
DOE OSTI2024

Use of physics to improve solar forecast: Part III, impacts of different cloud types

Cloud-type impacts present a great challenge to solar forecasting due to diverse and complex cloud-radiation interactions. This third part of our paper sequence seeks to address this challenge by quantifying the forecast accuracies under eight cloud types: cumulus (Cu), stratified clouds (St), altocumulus (Ac), altostratus (As), cirrostratus/anvil (Cr), cirrus (Ci), congestus (Co), deep convective clouds (Dc) across four physics-informed persistence models reported in Part I. To generalize the cloud impacts, the eight cloud types are further grouped into three cloud categories based on their common features: weak convective clouds, stratiform clouds, and strong convective clouds. Here, the decade-long (2001 ~ 2014) collocated measurements of irradiances and cloud types at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program South Great Plain (SGP) Central Facility site are used for model evaluation. Results reveal a clear performance hierarchy for global horizontal irradiance (GHI) and direct normal irradiance (DNI): best for weak convective clouds and cirrus, intermediate for stratiform clouds, and worst for strong convective clouds. Performance for diffuse horizontal irradiance (DHI) is less influenced by cloud types. Cloud albedo dominates all three irradiances for Dc, while both cloud albedo and cloud fraction are influential for other cloud types. A 12 %~33 % improvement in accuracy at 6-hour lead time compared to the benchmark smart model confirms the effectiveness of incorporating physics into the models for various cloud types; further improvements are expected by directly integrating cloud type information into forecasting models by modifying the physical formulation of cloud-radiation interaction, and/or using more advanced machine learning models.

14 SOLAR ENERGY↗
DOE OSTI2026

Development of A High-Resolution Dataset for Solar Resource Adequacy Studies

High-resolution, long-term solar dataset is essential for characterizing the variability of solar energy resources and for informing strategies that ensure grid reliability and resilience in grid systems with high levels of solar energy integration. We present the development of a new 4-km, hourly Earth system dataset for the contiguous United States (CONUS), using a statistical downscaling approach that integrates the National Solar Radiation Database (NSRDB) with regional Earth system model projections. The new high-resolution Earth system dataset includes key variables - GHI, DNI, DHI, surface air temperature, and wind speed - under two future scenarios. Preliminary results show a reasonable agreement with NSRDB observations, with nBias less than 1% for GHI across CONUS. The dataset is expected to support in-depth analyses of extreme weather impacts and provide input to resource adequacy for future energy systems with diverse generation sources.

14 SOLAR ENERGY↗
DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Solar Radiation Measurements (CRADA Final Report)

Long-term solar radiation measurements are important for understanding solar resource availability and variability for systems design and deployment of cost-effective solar resource technologies. Additionally high-quality traceable solar measurements are required to validate satellite-based solar resource datasets that provide high-resolution long-term data covering the US. The purpose and intent of this agreement is to collect long-term solar radiation and meteorological measurements from the state-of-the-art facility at the University of Arizona to meet the above needs.

14 SOLAR ENERGY↗
DOE OSTI2026

Impact of atmospheric turbulence on performance and loads of wind turbines: knowledge gaps and research challenges

Wind energy harvesting from the atmosphere takes place in the atmospheric boundary layer. The boundary layer shear and buoyancy create three-dimensional turbulent eddies spanning a range of scales that form a continuous forward cascade of kinetic energy to the smallest scales of motion where energy is dissipated. Large-scale atmospheric circulations modulate the boundary layer turbulence, characterized by coherence and intermittency. As wind turbines grow in size and the integrated control of both turbines and wind farms spans greater distances, the relationship between the scales of atmospheric turbulence and the design and operation of wind energy facilities has entered new territory. The boundary layer turbulence impacts both wind turbine power production and turbine loads. Optimizing wind turbine and wind farm performance requires an understanding of how turbulence affects both wind turbine efficiency and reliability. While the characteristics of atmospheric boundary layer turbulence have been observed and studied in detail over the last few decades, there are still significant gaps in our understanding of the impact of turbulence on wind power resources and wind farm operations. This paper outlines the current state of turbulence research relevant to wind energy applications and points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.

Kosović, Branko [Johns Hopkins Univ., Baltimore, M↗
DOE OSTI2026

Assessing Uncertainty in Solar Measurements: Key Findings From NLR's SUNI Application Across 89 Stations

The Solar Uncertainty Integrator (SUNI) study was developed by the National Laboratory of the Rockies (NLR's) to provide a standardized "bulk uncertainty processing" method for solar irradiance data, which are essential for the successful deployment of solar energy systems. This poster provides an overview of key findings from NLR's SUNI application across 89 stations.

14 SOLAR ENERGY↗
DOE OSTI2026

Solar Resource Measurements in Eugene, OR: Cooperative Research and Development Final Report, CRADA Number CRD-07-00252

Site-specific, long-term, continuous, and high-resolution measurements of solar irradiance are important for developing renewable resource data. These data are used for several research and development activities consistent with the NLR mission: establish a national 3-year climatological database of measured solar irradiances; provide high quality ground-truth data for satellite remote sensing validation; support development of radiative transfer models for estimating solar irradiance from available meteorological observations; provide solar resource information needed for technology deployment and operations. Data acquired under this agreement will be available to the public through NLR's Measurement & Instrumentation Data Center – MIDC (http://www.nlr.gov/midc) Or the Renewable Resource Data Center - RReDC (http://rredc.nlr.gov). The MIDC offers a variety of standard data display, access, and analysis tools designed to address the needs of a wide user audience (e.g., industry, academia, and government interests).

14 SOLAR ENERGY↗