In the quest to harness the power of the sun, accurate solar energy predictions are paramount. A recent study, led by J. I. Wiltink from the Royal Netherlands Meteorological Institute (KNMI), has shed light on improving the precision of satellite-derived global horizontal irradiance (GHI) data, a crucial factor for solar power generation and weather modeling. The research, published in the journal ‘Atmospheric Measurement Techniques’, delves into the intricacies of correcting parallax and shadow displacements in satellite imagery, offering promising insights for the energy sector.
Satellites, such as the Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI), provide a bird’s-eye view of Earth’s surface, enabling the estimation of solar irradiance. However, the accuracy of these estimates can be compromised by parallax and shadow displacements. Parallax refers to the apparent shift in the position of a cloud due to the satellite’s perspective, while shadow displacement occurs because the shadow of a cloud is not directly beneath it, contrary to the one-dimensional radiative transfer assumption used in satellite retrievals.
Wiltink and his team evaluated two primary methods to correct these displacements using data from a unique network of 99 pyranometers deployed during the HOPE field campaign in Jülich, Germany, in 2013. The first method involves geometric corrections based on retrieved cloud top heights. The second method relies on empirical collocation shifting, where the satellite grid is adjusted to maximize the correlation between satellite retrievals and ground-based observations.
“The time-step-averaged collocation shift correction generally yields the most accurate results,” Wiltink explained. However, this method has a significant drawback: its reliance on ground measurements. The geometric correction, which does not have this disadvantage, achieves the most accurate results when a combined parallax and shadow correction is performed. This combined approach reduces the GHI root mean square error by 11.7 W m⁻², a substantial improvement compared to the uncorrected retrieval.
The study also highlights that separate parallax or shadow corrections do not achieve the same level of accuracy and, in some cases, may even increase the error. Interestingly, the retrieval accuracy improves when the geometric correction is based on a reduced cloud top height, particularly in the presence of multilevel clouds.
The implications of this research are far-reaching for the energy sector. Accurate GHI data is essential for nowcasting solar power generation, allowing energy providers to optimize their operations and integrate solar energy more effectively into the grid. Moreover, precise satellite-derived GHI data is invaluable for validating weather and climate models, which in turn inform long-term energy planning and policy decisions.
As Wiltink noted, “GHI becomes increasingly sensitive to the applied correction at higher spatial resolutions, especially for variable cloud regimes.” This observation underscores the importance of advancing satellite technology and data processing techniques to meet the growing demands of the renewable energy sector.
In the pursuit of a sustainable energy future, every watt counts. The research led by Wiltink and his team at KNMI represents a significant step forward in improving the accuracy of solar irradiance data, ultimately enhancing our ability to harness the power of the sun. As the energy sector continues to evolve, the insights gained from this study will undoubtedly shape the development of more precise and reliable solar energy predictions.