发表机构
Beihang University; Shanghai Artificial Intelligence Laboratory; The Chinese University of Hong Kong; Tsinghua University（北京航空航天大学; 上海人工智能实验室; 香港中文大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
UniGIO提出统一生成框架，直接从稀疏不完整的全球原位观测建模天气，通过掩码生成统一预测、插补与生成，在Weather-5K上实现SOTA，精度、保真度和极端事件捕获分别提升11%、12%和5%。
AI 中文摘要
全球原位观测（GIO）通过稀疏的点站提供全球天气系统的精细、直接记录，使其成为捕捉卫星网格数据无法覆盖的局部和瞬态动态不可或缺的来源，并在数值天气预报、防灾和农业等关键领域发挥着关键作用。然而，GIO表现出强烈的时空不完整性，严重损害了准确、实时的原位天气建模。与现有方法等待带有额外引入误差的现成AI就绪数据不同，在这项工作中，我们探索了UniGIO，一种新颖的生成框架，用于直接从原生不完整的GIO中建模全球原位天气动态。通过从由掩码标注的观测数据生成缺失数据，它统一了在任意缺失比例下共存的预测、插补和生成任务。在缺失与观测之间，UniGIO通过观测混合器和事件对齐器捕获站点和区域级别的互补性，将离散观测扩散到连续空间中，在该空间中天气过程自然跨越多个站点。我们进一步利用自适应时间混合器建立具有模式偏移的时间依赖性，并通过专家混合结构跟踪混沌局部天气系统中的极端事件。稳态和极端事件在解码器中通过局部细化器进行适配。在最新最大的全球站点天气数据集Weather-5K上进行的大量实验验证了其最先进的性能，在准确性、保真度和极端事件捕获方面分别具有11%、12%和5%的优势，为GIO网络中的天气建模提供了一种新颖的整体解决方案。
英文摘要
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.
