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Abstract <p>Foundation models–large neural networks pretrained on heterogeneous Earth-system datasets and adapted to multiple tasks–are reshaping atmospheric and oceanic prediction. In this overview, we contrast this paradigm with task-specific supervised learning, review major atmospheric models (FourCastNet, ClimaX, Pangu-Weather, GraphCast, FengWu, FuXi, Prithvi WxC, AIFS, Aurora) and the first wave of oceanic models (WenHai, XiHe, LangYa), outline applications (forecasting, downscaling, statistical correction, subseasonal/seasonal prediction, analogs discovery, climate risk), and discuss challenges and perspectives including upcoming Russian initiatives.</p>

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Foundation Models of Ocean and Atmosphere in 2025: Milestones and Perspectives

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • M. A. Krinitskiy

摘要

Abstract

Foundation models–large neural networks pretrained on heterogeneous Earth-system datasets and adapted to multiple tasks–are reshaping atmospheric and oceanic prediction. In this overview, we contrast this paradigm with task-specific supervised learning, review major atmospheric models (FourCastNet, ClimaX, Pangu-Weather, GraphCast, FengWu, FuXi, Prithvi WxC, AIFS, Aurora) and the first wave of oceanic models (WenHai, XiHe, LangYa), outline applications (forecasting, downscaling, statistical correction, subseasonal/seasonal prediction, analogs discovery, climate risk), and discuss challenges and perspectives including upcoming Russian initiatives.