MeteoSage: An Operational Weather Agent Connecting Heterogeneous Observations to Forecasts and Service

Operational weather forecasting requires synthesizing heterogeneous observations and specialized forecast products to assess evolving weather hazards and deliver timely public services. Although AI has advanced individual forecasting tasks, integrating these capabilities into a coherent, traceable workflow remains challenging because of heterogeneous data semantics, conflicting evidence, and context-dependent forecasting strategies. We introduce , an agentic framework connecting observations, analysis, forecasting, and weather services through three complementary components: (i)  provides unified access to heterogeneous data and controlled tool execution; (ii)  links evolving assessments and unresolved discrepancies to the underlying observations to guide conflict assessment and resolution; and (iii)  distills eligible task records and feedback into reusable, context-specific guidance. We further introduce WxFlowBench, a benchmark built from multi-source observations, operational forecast products, and real-world meteorological reports. Its five tracks cover evidence-grounded analysis (T0), nowcasting over 0–3 h (T1), forecasting from 3 h to 15 d (T2), meteorological report generation (T3), and public weather services and emergency support (T4). Comprehensive evaluation on WxFlowBench shows that performance superior across all tasks. Our analysis identifies distinct failure modes involving forecast-model selection, sensor-modality mismatches, and forecast-pipeline composition. Dataset and code: https://github.com/useliz/MeteoSage.