Data-driven Combination of METAR Observations and CAMS Reanalysis Aerosols to Enhance Satellite Retrieval of Surface Solar Irradiance

Accurate solar irradiance forecasts are vital for photovoltaic (PV) power prediction, especially in tropical and subtropical regions affected by dust, wildfire smoke, and pollution. Yet, aerosol detection from satellites is often obstructed by clouds, AErosol RObotic NETwork (AERONET) stations are sparsely distributed, and climatological datasets cannot capture intra-day variability. Global products such as the Copernicus Atmosphere Monitoring Service (CAMS) provide broad coverage but miss local events due to coarse resolution and uncertainties in the underlying emission database. In this study, atmospheric parameters from METeorological Aerodrome Report (METAR) observations and CAMS reanalysis are used as inputs to data-driven models trained on normalized pseudo global horizontal clear sky irradiance ( GHI* _CS ) targets. Models tested include gradient boosting methods, Random Forests, neural networks, and a quantum variational circuit. The predicted global horizontal clear sky irradiance ( GHI _CS ) is then used in the Heliosat-3 method, which uses satellite-derived cloud index (CI) to estimate the all-sky global horizontal irradiance (GHI), for benchmarking against the all-sky GHI output of Heliosat-3 coupled with GHI _CS from the physics-based McClear model. Results show the largest root mean squared error (RMSE) reductions of 3–7% under visibility of 6–8 km, with Neural Network and eXtreme Gradient Boosting (XGBoost) achieving the highest overall gain (2.6%). During dust and sand events, performance improves substantially, with Light Gradient-Boosting Machine (LightGBM) achieving a 22% reduction. These findings demonstrate the value of GHI* _CS based machine learning approach for improving solar irradiance estimates in aerosol-rich environments.