One2N: Recursive refinement breaks the resolution barrier for Earth-system AI

Abstract

Earth-system AI models approach kilometre-scale resolution, but halving grid spacing quadruples the grid-point count and end-to-end training cost. We introduce One2N, a recursive incremental framework extending a coarse model through $N$ levels of cross-resolution residual correction. At each level the parent is frozen and excluded from backpropagation, so its activations and optimizer states are never retained; only a tier-specific module is trained. A single refinement level on the NeuralGCM-0.7${}^{\circ }$ baseline yields the two-level One2N-0.07${}^{\circ }$, trained on one 80 GB GPU. Against HRES-0.07${}^{\circ }$ and ERA5-0.25${}^{\circ }$ analyses it matches its parent's tropospheric skill while reducing upper-stratospheric errors and resolving finer structure. Tropical-cyclone track errors nearly match the parent's and are lower than Pangu-Weather-0.25${}^{\circ }$ at medium range; RMSE is lower than that model's on 93.46% of common targets. One2N thus turns an existing forecasting system, or an inexpensively trained coarse baseline, into a reusable foundation for high-resolution development, breaking the resolution barrier.

arXiv comment32 pages, 12 figures and 7 tables, including Supplementary Information