The Challenge to Derive Solar Surface Irradiance in Cloudy Southern West Africa from Satellite: Using Surface Measurements to Evaluate and Improve CM SAF's Retrieval

The DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) project aims to investigate the impact of the drastic increase in anthropogenic emissions in West Africa on the local weather and climate. In the framework of DACCIWA, an extensive collection of surface radiation data was compiled for southern West Africa. This provides a unique opportunity to evaluate satellite-derived datasets and models with ground-based measurements in this data sparse region. Since solar surface radiation is a crucial factor for boundary layer development and convection, we evaluate the solar surface irradiance from climate models and several satellite-based datasets. Amongst them the dataset SARAH developed by EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF) will be analysed in more detail. The estimation of solar surface irradiance is particularly difficult in regions that are covered by clouds during most of the day. This becomes apparent when comparing the retrieved solar surface irradiance with ground-based measurements. In average annual cycles, the quality of the CM SAF retrieval degrades considerably in the summer months, where the cloud cover is almost ubiquitous. Large biases are also found in winter, when haze due to natural and anthropogenic aerosol hinders the proper working of the algorithm. In this study, we identify the causes of the summer and winter biases in satellite-derived data records and propose a method to improve the derivation of solar surface irradiance for the CM SAF SARAH dataset.