AI in Hydrometeorology: Deep Learning for Satellite Precipitation Fusion and Flood Forecasting

This chapter reviews recent advances in the application of artificial intelligence for satellite precipitation data fusion, downscaling, and flood forecasting. Against the backdrop of global climate change and frequent extreme hydrometeorological events, particular emphasis is placed on the persistent challenges encountered by satellite precipitation products in complex terrain. AI and deep learning techniques have overcome many limitations of traditional forecasting methods by effectively addressing non-stationary spatiotemporal issues and delivering superior performance. In flood forecasting, rapid high-resolution simulations driven by AI not only significantly enhance the accuracy of numerical weather prediction (NWP) but also provide novel insights into the complex process through which precipitation uncertainty translates into hydrological risk. Emerging paradigms such as physics-informed neural networks exemplify the potential for an organic integration of process-driven hydrometeorology and data-driven AI, offering promising prospects for improved forecast accuracy and the development of adaptive warning systems, particularly in flash flood-prone regions such as the Tibetan Plateau. This chapter synthesizes the latest progress in understanding the error characteristics of satellite precipitation products, multi-source data fusion, downscaling, and flood forecasting. It advocates for the deep embedding of domain-specific physical mechanisms into AI frameworks, thereby providing a scientific foundation and decision-making support for flood control and disaster mitigation in the Upper Yangtze urban agglomeration, while simultaneously advancing hydrometeorological forecasting technologies on a global scale.