AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion
arXiv:2605.26130v1 Announce Type: new Abstract: Operational weather prediction at kilometer scales remains computationally prohibitive for traditional numerical weather prediction (NWP) models, limiting forecast access for applications in energy, agriculture, and disaster management that require fine-grained spatiotemporal detail. Here we introduce AirCast-SR, a foundation model for atmospheric super-resolution that downscales global AI weather forecasts from 0.25 degree (~28 km) to 1 km horizontal resolution at hourly temporal resolution, producing 67-hour forecasts of eight coupled surface v
Advances in AI, particularly foundation models and diffusion techniques, are enabling breakthroughs in complex scientific simulations that were previously computationally intractable.
Accurate, kilometer-scale weather prediction has significant economic and societal implications for critical sectors like energy, agriculture, and disaster management.
The ability to generate fine-grained weather forecasts efficiently will shift how industries plan operations and manage risks, potentially democratizing access to crucial atmospheric data.
- · AI compute providers
- · Renewable energy sector
- · Agriculture industry
- · Disaster management agencies
- · Traditional numerical weather prediction models
- · Sectors reliant on less precise forecasting
Operational weather prediction becomes significantly more accurate and accessible at local scales.
Improved forecasting leads to optimized resource allocation and reduced losses in weather-sensitive industries.
Enhanced climate modeling capabilities emerge, informing more effective long-term environmental strategies.
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Read at arXiv cs.LG