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UniGIO: Unified Generative Global In-situ Weather Modeling from Spatiotemporal Incomplete Observations

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Global In-situ Observation (GIO) provides fine-scale, direct records of the global weather system from sparse point stations, making it an indispensable source for capturing localized and transient dynamics beyond the reach of satellite gridded data, and playing a critical role in key fields such as numerical weather prediction, disaster prevention, and agriculture. However, GIO exhibits strong spatiotemporal incompleteness, severely impairing accurate and real-time in-situ weather modeling. Unlike existing methods waiting for completed AI-ready data with extra introduced errors, in this work,

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