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Sunairio’s weather and energy insights are built on a proprietary dataset of historical climate data that is downscaled to any coordinate and any height (from 2m to 300m).

Our data is generated using machine learning downscaling techniques that incorporate a mix of climate reanalysis, numerical weather prediction models, satellite information, and high-resolution topography.

The Sunairio climate dataset is rigorously validated, with measured accuracy that outperforms physics-based downscaling approaches. Contact us for validation documentation.

All Sunairio climate simulations are trained on our proprietary, hyperlocal climate data. The simulation methodology relies on new analytical methods that facilitate the replication of ultra-high dimensional systems, enabling a lifelike reproduction of complex hourly weather patterns and long-term climate trends.

Advanced machine learning and AI techniques then transform correlated weather patterns to energy resource predictions (electricity demand, wind generation, solar generation, distributed energy resources, and others). Our methods can use—but do not require—operational data, allowing you to simulate pre-construction assets or changes to customer/load resources.

Finally, we apply decades of energy market experience to deliver actionable insights into valuation, market, and grid reliability risks.