Adapting quantile mapping to bias correct solar radiation data
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Bailey, Maggie D. (ORCID:0000000347017849)↗
Engineering topics
Sengupta, Manajit
Publications and source records attributed to Sengupta, Manajit.
The influence of cloud cover on the reliability of satellite-based solar resource data
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54 ENVIRONMENTAL SCIENCES↗
Use of physics to improve solar forecast: Part Ⅲ, impacts of different cloud types
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54 ENVIRONMENTAL SCIENCES↗
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↗
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↗
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↗
Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Fourth Edition
As the world increasingly seeks low-carbon energy solutions, solar power emerges as the most abundant resource on our planet. However, the challenge of effectively harnessing this energy is crucial in the coming years. Solar energy applications such as photovoltaics, solar heating and cooling, and concentrating solar power use different technologies to capitalize on sunlight. Each system has unique capabilities and requirements, underscoring the need for reliable information about solar resources across diverse installations, from residential rooftops to large-scale power plants. This is especially important for substantial projects, often exceeding $1 billion in construction costs. Before embarking on such ventures, it is imperative to obtain accurate data concerning solar resource quality and reliability at specific sites. Developers require detailed historical information, including seasonal, daily, hourly, and, ideally, subhourly variability to effectively predict a power plant's annual performance. Without these vital data, financial analyses fall short. Moreover, with the growing adoption of distributed photovoltaics, integrating these generation sources becomes critical to maintaining grid reliability and stability. By accurately forecasting generation patterns, utilities and system operators can facilitate greater integration of solar energy, thus ensuring the operational stability of the grid. The complexity and importance of these issues have prompted the foremost experts in the field to collaborate under the auspices of the International Energy Agency's (IEA's) Photovoltaic Power Systems Programme (PVPS) Task 16 to publish this handbook, which summarizes state-of-the-art information about all these topics. The efforts focus on providing reliable data and insights that can help shape our investments in solar energy and drive a sustainable future.
14 SOLAR ENERGY↗
Standard Operating Procedure for Optimal Deployment of Meteorological Instrumentation Within the Solar Radiation Research Laboratory: 2024 Edition
The objective of the National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) is to collect and use high-quality solar radiation data sets for research leading to the widespread adoption of solar technologies. To appropriately populate and track the diverse array of instruments at the NREL-SRRL, NREL has established a Standard Operating Procedure (SOP) for optimal instrument deployment within the SRRL for both the Baseline Measurement System (BMS) and the Research Measurement System (RMS). Using best practices methodologies, the NREL-SRRL maintains a varied and extensive array of solar monitoring equipment to test, evaluate, and characterize the solar sensors used by federal and international agencies as well as the solar industry to determine the solar resource. The SOP provides the industry with guidance for solar resource assessment and is used for procedures in the long-term continuous monitoring of legacy instruments alongside state-of-the-art instruments. Based on the SOP, instruments are annually evaluated for continued deployment. Instruments that do not meet the SOP criteria are decommissioned, and new instruments that meet the criteria are deployed. Streamlining and optimizing the use of this facility ensures that the lab continues to be a world-leading solar calibration and measurement facility. This 2024 edition includes updates to the appendices to reflect the instrument changes from one year to another.
14 SOLAR ENERGY↗
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↗
A Fast All-Sky Radiation Model for Solar Applications with Narrowband Irradiances on Tilted Surfaces (FARMS-NIT): Part II. The Cloudy-Sky Model
The Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMSNIT) reported in Part I of this study is enhanced to include the requirements for cloudy-sky conditions. Surface radiances in 2002 narrow-wavelength bands from 0.28 to 4.0 μm are analytically computed by solving the radiative transfer equation for five independent photon paths accounting for clear-sky absorption, Rayleigh scattering, and cloud absorption and scattering. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) is used to provide the optical thickness of the clear-sky atmosphere. Unlike Part I, which approximates the computation of aerosol scattering using the single-scattering phase function, the cloud transmittance and reflectance are efficiently retrieved from a comprehensive look-up table pre-computed by a 32-stream DIScrete Ordinates Radiative Transfer (DISORT) model for possible cloud conditions as well as solar and viewing geometries. A resolution analysis is performed to assess the optimal balance between the computational efficiency and accuracy in the development of the look-up table. Model simulations by DISORT and TMYSPEC are used to evaluate the performance of FARMS-NIT under cloudy-sky conditions. Compared to DISORT, FARMS-NIT yields 2–3% uncertainties on average, but it substantially reduces the computational time because of the independent computation of cloud properties and the implementation of the look-up table. In contrast to TMYSPEC, which uses successive steps to empirically compute plane-of-array (POA) irradiances and spectral irradiances, FARMS-NIT directly solves spectral radiances from the radiative transfer equation, which profoundly increases the accuracy in surface irradiances, especially over inclined photovoltaics (PV) panels.
Xie, Yu↗
An Assessment of New Satellite Data Products for the Development of a Long-Term Global Solar Resource at 10-100 km
A project representing an effort to reprocess the NASA based solar resource data sets is reviewed. The effort represented a collaboration between NASA, NOAA, NREL and the SUNY-Albany and aimed to deliver a 10 km resolution, 3-hourly data set spanning from 1983 through near-present. Part of the project was to transition project capability to NREL for annual processing to extend data set. Due to delays in the key input project called ISCCP, we evaluate only Beta versions of this data set and also introduce the potential use of another NASA Langley based cloud data set for the CERES project. The CERES project uses these cloud properties to compute global top-of-atmosphere and surface fluxes at the 1x1 degree resolution. Here, we also briefly discuss these data sets in potential usage for solar resource benchmarking.
Stackhouse, Paul W., Jr.↗
Progress Towards Deriving an Improved Long-Term Global Solar Resource
This paper describes an ongoing project to provide the National Renewable Energy Laboratory (NREL) with a global long-term advanced global solar mapping production system for improved depiction of historical solar resources and to provide a mechanism for continual updates. This new production system is made possible by the efforts of NASA and NOAA to completely reprocess the International Satellite Cloud Climatology Project (ISCCP) data set that provides satellite visible and infrared radiances together with retrieved cloud and surface properties on a 10 km, 3-hourly basis beginning July 1983. We provide a general overview of this project, samples of the new solar irradiance mapped data products, and comparisons to surface measurements. Samples of the use of the SUNY-Albany solar irradiance algorithm applied to the ISCCP data show very good agreement with high quality surface measurements. We identify the next steps in the production of the data set.
Cox, Stephen J.↗
Towards an Improved High Resolution Global Long-Term Solar Resource Database
This paper presents an overview of an ongoing project to develop and deliver a solar mapping processing system to the National Renewable Energy Laboratory (NREL) using the data sets that are planned for production at the National Climatic Data Center (NCDC). NCDC will be producing a long-term radiance and cloud property data set covering the globe every three hours at an approximate resolution of 10 x 10 km. NASA, the originators of the Surface meteorology and Solar Energy web portal are collaborating with SUNY-Albany to develop the production system and solar algorithms. The initial result will be a global long-term solar resource data set spanning over 25 years. The ultimate goal of the project is to also deliver this data set and production system to NREL for continual production. The project will also assess the impact of providing these new data to several NREL solar decision support tools.
Stackhouse, Paul W.↗