Evaluating parallax and shadow correction methods for global horizontal irradiance retrievals from Meteosat SEVIRI

Abstract. The transition towards an energy supply with a high share of renewable energy calls for accurate global horizontal irradiance (GHI) observations. Satellite-derived GHI covers large geographical areas, making it an excellent data source for nowcasting solar power generation and the validation of weather and climate models. To obtain a good match between satellite-derived GHI and surface observations of GHI, a precise geolocation of the satellite GHI is an essential factor in addition to the accuracy of the retrieval. The geolocation of satellite retrievals is affected by parallax, a displacement between the actual and apparent position of a cloud, as well as by a displacement between the actual position of a shadow and the cloud casting that shadow. This study evaluates different approaches to correct Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) retrievals for parallax and cloud shadow displacements using ground-based observations from a unique network of 99 pyranometers deployed during the HOPE field campaign in Jülich, Germany, in 2013. The first method provides geometric corrections for the displacements calculated using retrieved cloud top heights. The second method relies on empirical collocation shifting. Here, the collocation shift of the satellite grid is determined by maximizing the correlation between the satellite retrievals and ground-based observations. This optimum shift is determined either based on daily or time step averaged correlations. The time step averaged collocation shift correction generally yields the most accurate results, but the drawback of this method is its reliance on ground-based observations. The geometric correction, which does not have this limitation, achieves the most accurate results if a combined parallax and shadow correction is performed. At higher spatial resolutions, the GHI retrieval accuracy becomes increasingly sensitive to the applied correction. At the SEVIRI standard native resolution of 3 x 3 km² (SR), the root mean square error (RMSE) is reduced by 6.3 W m^-2 (6.0 %), going from the uncorrected to combined geometrically corrected retrieval. At a threefold higher resolution (HR), this difference increases to 11.7 W m^-2 (10.8 %). Cloud heterogeneity also strongly influences the sensitivity to geolocation accuracy and spatial resolution. Variable cloud regimes exhibit a higher sensitivity to geolocation corrections compared to less variable regimes. As an illustration, the mean improvement in HR RMSE between the uncorrected retrieval and the time step optimal shift is 13.3 W m^-2 (10.9 %) for the stratocumulus cloud regime, while for the cirrus cloud regime the improvement is negligible.