Assessing renewable wind and solar energy yield with gridded climate datasets

Abstract Gridded meteorological datasets—reanalyses and climate simulations—are increasingly central to renewable energy yield assessments, offering complete, physically consistent atmospheric variables required to transform resource data into power output. This review delivers the first holistic synthesis of methodologies and uncertainties across the full modeling chain for both wind and solar energy, from resource characterization to energy yield and bankability metrics. It emphasizes the rising use of long-term, spatially resolved data to improve site selection, financial planning (P50/P90), and climate risk assessment. The review addresses critical modeling steps—vertical extrapolation, air density correction, irradiance decomposition—and quantifies sources of uncertainty inherent in each. By integrating historical and climate-future perspectives, it defines best practices for robust, transparent, and reproducible energy yield assessments.