This study examines the modeling uncertainty of wind resource data stemming from the use of various planetary boundary layer (PBL) parameterizations available in the Weather Research and Forecasting (WRF) model. WRFbased wind simulations spanning 20 years at 3-km resolution using 11 different PBL schemes are used to objectively investigate the uncertainty in modeling wind speed for land-based wind (LBW) and offshore wind (OSW) locations in Puerto Rico. The uncertainty in the wind modeling for the 20-year dataset is quantified using the spread index (SI) and standard deviation (SD). For virtual LBW and OSW sites, the SI and SD values are analyzed as calculated across various spatial and temporal scales. Because the PBL's atmospheric stability conditions can be characterized into two dominant categories, the study focuses on analyzing the SI and SD for daytime (mainly unstable PBL conditions) and nighttime (mainly stable PBL conditions). For wind shear (10 m-200 m) at the OSW and LBW sites, WRF-based numerical experiments indicate the following SI (or SD) ranges: 39 %-94 % (0.74 m/s-1.44 m/s) during the daytime for OSW, 50 %-75 % (0.68 m/s-1.19 m/s) during the daytime for LBW, 37 %-60 % (0.73 m/s-1.12 m/s) during the nighttime for OSW, and 57 %-143 % (0.65 m/ s-1.43 m/s) during the nighttime for LBW. While a high SI is observed when modeling LBW during the nighttime, there are notable modeling uncertainties during the daytime on the leeward side of the orographic barriers for Puerto Rico.

Bias correction is a common preprocessing step applied to climate model data before they are used for further analysis. This article introduces an efficient adaptation of a well-established bias correction method, quantile mapping, for global horizontal irradiance (GHI) that ensures corrected data are physically plausible through incorporating measurements of clear-sky GHI. The proposed quantile mapping method is fit on reanalysis data to first bias correct for regional climate models (RCMs) and is tested on RCMs forced by general circulation models (GCMs) to understand existing biases directly from GCMs. Additionally, we adapt a functional analysis of variance methodology that analyzes sources of remaining biases after implementing the proposed quantile mapping method and consider biases by climate region. The proposed method is able to correct for biases due to seasonality on a monthly time scale as well as produce physically plausible values in the corrected data when compared to observed GHI. Analysis shows that biases from GCMs are generally not geographically specific and that RCMs may contribute strong biases to GHI that need to be corrected for. This analysis is applied to four sets of climate model output from NA-CORDEX and compared against data from the National Solar Radiation Database (NSRDB) produced by the National Renewable Energy Laboratory.

The National Solar Radiation Database (NSRDB) provides comprehensive global solar resource data at a high temporal and spatial resolution. The NSRDB employs satellite-based solar modeling to retrieve cloud properties and subsequently compute solar radiation. The other input parameters—including aerosol optical properties, precipitable water vapor, surface albedo, temperature, and pressure—are also employed by the model. The NSRDB data were recently updated using physical solar model (PSM) version 4, which includes an improved gap-filling algorithm, based on machine-learning techniques, for the missing cloud properties, corrections of solar position calculations, an enhanced algorithm for computing land-surface albedo over snow or ice surfaces, and a physics-based model for computing DNI. The updated PSM was used to generate solar radiation data for 2021-2023, including global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). This model was also employed to reprocess all previous NSRDB data from 1998-2020. The NSRDB was validated using high-quality measurements from surface sites, including NOAA’s the Surface Radiation Budget (SURFRAD). According to data for 2019-2023, the NSRDB processed by the latest PSM has mean bias error (MBE) within 5% and 8%, respectively, for GHI and DNI. This study provides users with the latest NSRDB information and outlines plans for ongoing development and updates.

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. The decade-long (2001 similar to 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 %similar to 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.

Satellite-based solar resource data are generally developed and validated using binary cloudiness categories: either clear or cloudy. To investigate the reliability of solar resource data in partially cloudy conditions, we estimate cloud fraction using two distinct algorithms: a physical retrieval using surface observed global horizontal irradiance (GHI) and direct normal irradiance (DNI) and a temporal average of cloudiness estimated using observed DNI. Our analysis reveals a significant presence of scattered clouds, broken clouds, and mismatches between satellite- and surface-based cloud information at 17 surface sites across the contiguous United States, though confidently clear and cloudy conditions collectively occur more than 70% of the time. Solar radiation is computed using the National Solar Radiation Database (NSRDB) algorithm and validated using surface observations. Our findings suggest that, in the presence of scattered clouds, NSRDB data for clear-sky conditions can be significantly overestimated. Under 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 fractional cloudiness is identified using 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 radiative transfer modeling that accounts for amplified cloud reflection and fractional cloudiness.

Accurate solar irradiance data is crucial for solar energy application, particularly in understanding system performance and for project feasibility assessment. There are multiple solar measurement networks around the world such as the BSRN and the Atmospheric Radiation Measurement (ARM) Climate Research Facility that provide high-quality radiometric data, but the integrity of this data is heavily dependent on factors such as radiometer type, calibration, installation and operational maintenance which contribute to the uncertainty of the data. The quantification of measurement uncertainty is crucial, and NREL has developed the Solar Uncertainty Integrator (SUNI) software which adheres to the internationally accepted Guide to the Expression of Uncertainty in Measurement (GUM). The SUNI software has been designed specifically to integrate data quality analyses and measurement uncertainty estimates for global horizontal, direct normal, and diffuse horizontal irradiance data. It processes this information at intervals ranging from one to sixty minutes, resulting in a user-friendly experience that accommodates various site-specific factors affecting data quality. The SUNI software has been developed in Python and a graphical user interface has also been developed. NREL applied the method to one-minute resolution BSRN and ARM irradiance measurements and this study showcase these findings.

International standards for radiometry provide essential guidelines and frameworks that ensure consistency, accuracy, and reliability in the measurement of solar radiation and associated parameters. These standards are vital for solar energy applications, climate research, and studies on weathering and durability. The National Renewable Energy Laboratory (NREL) has taken a leading role in various international subcommittees, including the ISO/TC180 SC1 Climate - Measurement and Data and the ASTM G03.09 Radiometry subcommittee. Recently, NREL and its partners have been actively involved in updating and developing multiple standards to incorporate the latest advancements in knowledge, science, and technology. This paper outlines some key aspects of these updates and their practical applications.

The fourth edition of the handbook serves as an all-encompassing resource for solar energy professionals, academia, and other stakeholders. The handbook is produced as a collaborative effort of 51 experts from 15 countries and contains the combined knowledge and experience of these foremost leaders in the field. This updated edition provides best practices for measuring, modeling, forecasting, analyzing, and utilizing solar resource data across various applications in solar energy. As the field of solar energy rapidly advances, so too does the understanding of solar resources. The fourth edition has undergone significant revisions, as evidenced by substantial updates across all existing chapters and the addition of two new chapters, bringing the total to 12 chapters. Recognizing that solar resources are fundamental to solar energy applications, this edition aims to furnish users with vital information that can lower costs and expedite solar deployment. This paper will introduce the audience to the fourth edition and highlight key new information that is crucial for users.

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.

The current advanced geostationary imagers including the GOES-R ABI and MTG FCI instruments offer significant improvements in terms of spatio-temporal resolution compared to previous instruments, featuring pixel sizes for solar channels down to 500x500m2, and scan frequencies up to 1 per min. While these capabilities enable us to better resolve small-scale variability in clouds and radiation, our understanding of the practical benefits for monitoring cloud development and retrieving surface solar irradiance remains limited. One key reason is the limited representativity of many ground-based remote sensing observations serving as potential reference, which are point-like in nature. In contrast, satellite-derived quantities correspond to extended spatial domains. To improve our knowledge about the small-scale structure and variability of clouds and its influence on solar radiation, the Small-Scale Variability of Solar Radiation (S2VSR) campaign was conducted at the ARM Southern Great Plains (SGP) site in summer 2023. A unique sensor network consisting of 60 autonomous pyranometer stations developed at the Leibniz Institute for Tropospheric Research was deployed at the SGP site for a 12-week period. Stations were distributed across a 6x6 km2 domain centered around the ARM SGP Central Facility. Together with operational ARM measurements including cloud profiling and a stereo-photogrammetric 4D reconstruction of clouds, this campaign offers an unprecedented dataset for studying cloud-induced small-scale variability in solar irradiance, resolving fluctuations down to the second- and decameter-scale. In the present contribution, a preliminary analysis of the benefits of 500m-resolution retrievals based on the GOES-R ABI imager using the S2VSR data will be given. Specifically, the deviation of satellite retrievals of surface solar irradiance from single-site measurements caused by the limited representativity will be quantified. An estimate of the instantaneous retrieval uncertainty will be given for different cloud situations. Also, the effects of navigation accuracy and the impact of two different parallax correction strategies will be quantified.

Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.

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.