Reduction of systematic differences of LSASAF shortwave solar radiation fluxes using neural networks

To improve the process of solar energy production, we can utilize the downward shortwave flux (DSSF) measurement, which constitutes a part of the satellite derived total and diffuse downward surface shortwave flux (MDSSFTD) product. MDSSFTD is issued by the Satellite Application Facility on Land Surface Analysis (LSA SAF). However, its direct application in this area is inhibited by potential systematic errors in the DSSF product. Therefore, this has to be addressed before the DSSF can be used downstream. To this end, we implemented a neural network-based post-processing procedure that uses previous temporal DSSF observations and additional predictors, such as cloudiness and time of day, to generate a corrected DSSF value. The ground truth for this regression task are the in-situ measurements across a variety of locations in Slovenia. Additionally, the neural network produces DSSF estimates in terms of quantiles, providing an uncertainty estimate of the corrected prediction itself. We verified our new method on the aforementioned region over a period offour years. We found that our neural network approach successfully reduces the presence of systematic differences present in the DSSF. Additionally, the neural network method outperforms a baseline look-up-table approach in termsof multiple criteria, such as mean absolute error, bias, and error variability.