Otter Weather: Skillful and computationally-efficient medium-range weather forecasting

State-of-the-art medium-range AI weather models rival traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts access for under-resourced groups and severely limits fast model iteration. We introduce Otter, a highly efficient spatiotemporal forecasting model designed to democratise high-performance weather prediction with AI. Otter is evaluated on ERA5 reanalysis data using the standard WeatherBench protocols where it significantly advances the skill-compute Pareto frontier. The deterministic version outperforms the best NWP baseline by 9.6\% at a 24-hour lead time while requiring fewer than 3.5 A100-days for training. It provides a 2x efficiency gain over lightweight AI models and a 100-fold reduction in compute compared to resource-intensive frontier architectures.