While AI has achieved remarkable advancements in areas such as image recognition and natural language processing, its application in Earth and environmental sciences is still emerging. Unprecedented data from satellites, remote sensors, and in-situ measurements offers new opportunities to improve physics-based model forecasts of environmental systems with AI and to gain deeper insights. However, extreme systems as weather and climate events, pose distinct challenges for AI, such as limited sampling of rare events, non-trivial data augmentation, errors-in-variables, and complexities of transfer learning across diverse tasks. In this talk, I will explore these challenges and showcase AI architectures designed to address them. I will use specific examples of forecasting dust storms, precipitation extremes, and drought events in the Middle East.
