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

Global In-situ Observation (GIO) provides fine-scale, pointwise weather measurements beyond gridded satellite products, supporting numerical weather prediction and disaster prevention. However, severe spatiotemporal incompleteness in GIO leads existing methods to rely on AI-ready preprocessing or massive global multi-source data, limiting accurate, timely, and efficient in-situ weather services. To address this gap, we propose UniGIO, a unified generative framework that exploits station-centered observational complementarity and decouples local extremes from steady evolution, unifying forecasting, imputation, and generation directly from incomplete GIO. UniGIO progressively builds complementarity from discrete station interactions to continuous local fields. It captures missing-aware sequential evolution in a shared state space, while separate expert representations support extreme-pattern adaptation and global pattern recognition. These mechanisms form a lightweight CVAE suitable for distributed stations, explicitly modeling missing-state uncertainty for extreme-event assessment and generation. Experiments on Weather-5K, the largest up-to-date GIO dataset, demonstrate SOTA performance, with average gains of 11%, 12%, and 5% in accuracy, fidelity, and extreme-event capture, advancing unified weather modeling from incomplete station observations.