Tadpole-2D: From PDE Foundation Model to Global Weather Forecasting

The development of AI-based weather forecasting models generally demands significant computational resources and extensive data storage. Recent advances in foundation models for partial differential equations (PDEs) have enabled efficient synthetic datasets and storage-free training pipelines, which may address these challenges. In this work, we present Tadpole-2D, a versatile foundation model for two-dimensional (2D) PDEs. It is pretrained to reconstruct 2D slices from diverse three-dimensional PDE data generated online, and its downstream performance is verified on a challenging PDE dynamics learning task. We extend the model to global weather prediction with adaptations enabling it to process atmospheric data on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) grid. We choose HEALPix for its superior numerical properties over equiangular grids used in most flagship weather systems. In short-range weather forecasting, the resulting model outperforms state-of-the-art PDE foundation models of comparable size. To our knowledge, ours is the first work in which a PDE foundation model is demonstrated to transfer successfully to global weather forecasting.