arXiv:2606.26421v1 Announce Type: new Abstract: State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts usage for under-resourced groups and severely limits fast model iteration. Here we develop Otter Weather, a highly efficient spatiotemporal forecasting model designed to democratise high-performance weather prediction with AI. Evaluated on ERA5 reanalysis data at 1.5{\deg} resolution using standard WeatherBench protocols, the Otter family significantly advances the skill-compute Pareto
The increased computational efficiency of AI models like Otter Weather is a direct response to the massive resource requirements of current state-of-the-art AI, making advanced AI capabilities more accessible.
Democratizing high-performance AI weather prediction fosters broader adoption and allows under-resourced groups to leverage advanced forecasting, enhancing preparedness and resilience.
Access to highly accurate medium-range weather forecasting, previously confined to large, well-funded entities, becomes more widespread and less computationally intensive.
- · Developing nations
- · Agricultural sector
- · Logistics companies
- · Climate adaptation initiatives
- · Legacy weather prediction services (potentially)
- · Cloud providers dependent on high AI compute spend
More accurate and accessible weather forecasts lead to better planning and reduced economic losses from weather events.
Reduced barriers to entry for advanced weather modeling could spur innovation and competition in climate tech.
Enhanced global weather prediction capabilities may contribute to better coordinated international responses to climate challenges and disasters.
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Read at arXiv cs.LG