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

Researchers introduced UniGIO, a generative framework that models global in-situ weather dynamics directly from spatiotemporally incomplete station observations, according to an arXiv paper (2609.22217v1). UniGIO unifies forecasting, imputation, and generation under arbitrary missing ratios using an Observation Mixer, Event Aligner, Adaptive Temporal Mixer, and a Mixture-of-Experts structure, and experiments on the Weather-5K global station dataset show state-of-the-art performance with 11%, 12%, and 5% advantages on accuracy, fidelity, and extreme event capture respectively.

arXiv:2609.22217v1 Announce Type: new Abstract: 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, we explore UniGIO, a novel generative framework for directly modeling global in-situ weather dynamics from native incomplete GIO. By generating missing data from observed ones annotated by masks, it unifies the coexisting forecasting, imputation, and generation under arbitrary missing ratios. Between the missing and observed, UniGIO captures station and region level complementarity through the Observation Mixer and Event Aligner, which diffuse discrete observations into continuous spaces where weather processes naturally span multiple stations. We further establish temporal dependencies with pattern shifts using the Adaptive Temporal Mixer, and track extreme events in chaotic local weather systems through a Mixture-ofExperts structure. Steady and extreme events are adapted in decoder by a Local Refiner. Extensive experiments on the up-todate largest global station weather dataset Weather-5K validate its SOTA performance with 11%, 12%, and 5% advantages on accuracy, fidelity, and extreme event capture, delivering a novel holistic solution for weather modeling in GIO networks.