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

• Published in: Renewable & sustainable energy reviews Vol. 208; p. 115070
• Main Authors: Xie, Yu, Sengupta, Manajit, Yang, Jaemo, Habte, Aron, Buster, Grant, Benton, Brandon, Foster, Michael, Heidinger, Andrew, Liu, Yangang
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2025; Elsevier
• Subjects: cloud fraction; PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; satellite; SOLAR ENERGY; solar radiation
• ISSN: 1364-0321
• DOI: 10.1016/j.rser.2024.115070

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. 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. •Cloud fraction is estimated using surface-based solar radiation measurements.•The uncertainty in solar resource data is analyzed in various cloudiness categories.•There is significant overestimate of clear-sky radiation due to scattered clouds.•Physics-based DNI model has improved performance in cloudy conditions.