A Rational Function Model for the Decomposition of Global Solar Irradiance into Direct and Diffuse Components

Solar irradiance decomposition models are essential for estimating direct normal irradiance and diffuse horizontal irradiance when only global horizontal irradiance measurements are available. While recent studies have increasingly relied on machine learning techniques, analytical models remain attractive because of their transparency, low computational cost, and ease of implementation. This study proposes a rational function model for the decomposition of global horizontal irradiance into its direct and diffuse components. The model parameters were calibrated using four years of high-quality measurements from four stations of the Baseline Surface Radiation Network (BSRN) representing different climatic conditions. The resulting parameterization combines mathematical simplicity with the ability to reproduce the nonlinear dependence of irradiance components on atmospheric conditions. The model was subsequently validated using independent BSRN datasets not involved in the calibration stage. Its performance was assessed through standard statistical indicators and compared with results reported in the recent literature, including both classical analytical decomposition models and machine-learning approaches. The results demonstrate that carefully calibrated analytical formulations remain a viable alternative to more complex data-driven methods. The proposed rational model provides an effective compromise between accuracy, interpretability, and computational efficiency, making it suitable for solar energy applications requiring reliable irradiance component estimates.