Temporal Variability of Surface Solar Irradiance as a Function of Satellite-retrieved Cloud

Studies of the impact of renewables on the electrical transmission grid are needed as power production from renewable energy resources increases. These studies require estimates of high temporal and spatial resolution power output under various scenarios. Satellite-based solar resource estimates are the best source of long-term irradiance data but are generally of lower temporal and spatial resolution than needed and thus require downscaling. Likewise, weather forecast models cannot provide high spatial or temporal irradiance predictions. Downscaling requires information about solar irradiance variability in both space and time, which is primarily a function of cloud properties.

In this study, we analyze the relationships between the temporal variability of surface solar irradiance and satellite-based cloud properties. One-minute resolution surface solar irradiance data were obtained from the National Oceanic and Atmospheric Administration’s Surface Radiation (SURFRAD) network. These sites are distributed across the United States to cover a range of meteorological conditions. Cloud information at a nominal 4 km resolution and half hour intervals was retrieved from NOAA’s Geostationary Operation Environmental Satellites (GOES). The retrieved cloud properties were then used to select and composite irradiance data from the measurement sites in order to identify the cloud properties that exert the strongest control over short-term irradiance variability. The irradiance variability was characterized using statistics of both the irradiances themselves and of irradiance differences computed for short time scales (minutes). The relationships derived using this method will be presented, comparing and contrasting the statistics computed for the different cloud properties. The implications for downscaling irradiance from satellites or forecast models will also be discussed.