2025/01/08 by Jean Rabault, ML Sætra, Rabault, J +7
Computer Science · Economics, Econometrics and Finance · #Atmospheric and Oceanic Physics (physics.ao-ph) #Economic and Technological Innovation #FOS: Physical sciences #Geochemistry and Geologic Mapping
paper · pdf · doi:10.48550/arxiv.2501.04381
openalex publication_date 2025/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the present work, we collect solar irradiance and atmospheric condition data from several products, obtained from both numerical models (ERA5 and NORA3) and satellite observations (CMSAF-SARAH3). We then train simple supervised Machine Learning (ML) data fusion models, using these products as predictors and direct in-situ Global Horizontal Irradiance (GHI) measurements over Norway as ground-truth. We show that combining these products by applying our trained ML models provides a GHI estimate that is significantly more accurate than that obtained from any product taken individually. Using the trained models, we generate a 30-year ML-corrected map of GHI over Norway, which we release as a new open data product. Our ML-based data fusion methodology could be applied, after suitable training and input data selection, to any geographic area on Earth.