Roles of Machine Learning in Applied Weather Forecasting

Explore the transformative applications of machine learning in operational weather forecasting through this comprehensive lecture by Sue Ellen Haupt. Discover how artificial intelligence and data-driven approaches are revolutionizing traditional meteorological prediction methods, from improving forecast accuracy to enhancing computational efficiency. Learn about the integration of machine learning algorithms with physics-based models, the development of surrogate models for complex atmospheric processes, and the practical implementation of AI-powered forecasting systems in real-world meteorological operations. Examine specific case studies demonstrating how machine learning techniques address challenges in parameterization of localized weather phenomena, uncertainty quantification, and ensemble forecasting. Understand the current state of operational ML-based weather models, including transformer-based architectures like FourCastNet and graph neural network approaches such as GraphCast, and their adoption by national meteorological agencies. Gain insights into the advantages and limitations of data-driven forecasting compared to traditional numerical weather prediction models, and explore future directions for machine learning applications in atmospheric sciences and climate modeling.