AI for Weather Nowcasting: Opportunities and Challenges

Weather nowcasting, defined as forecasting weather phenomena on time scales from minutes to several hours, is critical for mitigating the impacts of high-impact events such as flash floods, severe convective storms, and extreme precipitation. Traditional nowcasting approaches based on radar extrapolation and convection-permitting numerical weather prediction (NWP) exhibit fundamental limitations in representing rapid storm evolution, convective initiation, and localized extremes at short lead times. Recent advances in artificial intelligence (AI) and deep learning have enabled a new generation of nowcasting systems that learn complex spatiotemporal relationships directly from high-resolution radar, satellite, lightning, and NWP data. This paper provides a comprehensive review of AI-based precipitation nowcasting, covering data sources, model architectures, and evaluation methodologies. We discuss deterministic and probabilistic approaches, including convolutional recurrent networks, encoder–decoder convolutional neural networks, transformers, generative adversarial networks, diffusion models, and emerging physics-informed and hybrid AI–NWP systems. Opportunities such as improved short-lead forecast skill, multi-sensor fusion, probabilistic decision support, and enhanced forecast equity are examined alongside key challenges related to data quality, class imbalance, generalization, interpretability, and operational deployment. Finally, we highlight current research frontiers and methodological trends, outlining open challenges and promising directions for future AI-driven nowcasting systems at the PhD level and beyond.