Improvement of satellite-derived surface solar irradiance estimations using spatio-temporal extrapolation with statistical learning

Estimations of solar surface irradiance (SSI) derived from meteorological satellites are widely used by various actors in the solar industry. However, even state-of-the-art empirical and physical SSI retrieval models exhibit significant errors; the estimations provided by these models are thus traditionally corrected using ground-based measurements of SSI as references. The literature is rich with such correction methods, often called adaptation techniques. Most of the proposed models, however, are local or site-specific, i.e., they do not extrapolate the correction in space and are only applicable to the location of the ground-based measurements. In this work, we propose a novel global adaptation technique, that can extrapolate the correction in both space and time. To that end, we leverage (1) a dense network of measurement stations across France, (2) a relatively large number of predictors, and (3) a non-linear, sophisticated regression algorithm, the Extreme Gradient Boosting. The model is applied to the HelioClim3 database; its performance is benchmarked against raw HelioClim3 estimations, and alternative, simpler adaptation techniques.Our analysis shows that this global model significantly improves satellite-derived SSI estimations from the HelioClim3 database, even when the evaluation is carried out on measurement stations that were not part of the training set of the algorithm. Our proposed model also outperforms all tested alternative global adaptation techniques.These results suggest that global adaptation techniques leveraging advanced machine learning and high dimensionality have the potential to significantly improve satellite-derived SSI estimations, notably more than traditional adaptation approaches. There is certainly room for improvement, but the development of such techniques is a promising research topic.