AI RESEARCH
Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss
arXiv CS.AI
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ArXi:2604.18727v1 Announce Type: cross Machine learning emulators have shown extraordinary skill in forecasting atmospheric states, and their application to global ocean dynamics offers similar promise. Here, we adapt the GraphCast architecture into a dedicated ocean-only emulator, driven by prescribed atmospheric conditions, for medium-range predictions. The emulator is trained on NOAA's UFS-Replay dataset. Using a 24 hour time step, single initial condition, and without using autoregressive.