Weather Text as Observation for Meteorological Field Generation via a Unified Multimodal Model

Natural-language weather descriptions have a long history as meteorological records but have rarely been exploited as an information source for atmospheric state inference, particularly for reconstructing meteorological fields. Directly conditioning field generators on weather text, however, provides weak constraints because descriptions are ambiguous and are sparsely aligned with gridded meteorological observation. We address this problem by converting weather descriptions into quantitative meteorological pseudo-observations before field generation. To support this formulation while providing dense supervision, we construct MeteoText, a large-scale dataset that aligns daily city-level weather descriptions with meteorological variables and fields, and define five tasks spanning weather-text understanding and field generation. We further propose MeteoOmni, a unified multimodal model with a Value Probe for text-to-value mapping and Text-Derived Observation Guidance for injecting the resulting psedo-observations as local constrains during generation inference. Experiments show that MeteoOmni consistently outperforms strong baselines across all five tasks and generalizes zero-shot to real-world meteorological warnings. More importantly, we demonstrate that weather texts can serve as pseudo-observations to improve local generation, with their benefits extending to the full meteorological field.