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Mountain regions concentrate significant renewable energy potential-including approximately 70% of global hydropower capacity-yet, the meteorological datasets essential for planning systematically fail in complex terrain. Using the Alps as a globally transferable testbed, we evaluate datasets spanning the seamless chain from global reanalyses (ERA5, 31 km) through convection-permitting reanalyses (ARA, 2.5 km) to kilometer-scale digital twins (4.4 km) and AI-driven forecasting systems, assessing their performance against energy-relevant phenomena including population-weighted temperature extremes, wind gusts, and compound events. No single dataset excels universally; resolution alone does not ensure accuracy without adequate physics representation. Sparse mountain observations above 1,500 m fundamentally limit validation. We propose six recommendations-from multi-dataset ensemble approaches to a pan-Alpine transboundary reanalysis-providing actionable guidance for energy planners, meteorological service providers, and policymakers navigating the energy transition in complex terrain worldwide.
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Schicker et al. (2026) studied this question.
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