AI for Sustainable Weather Forecasting

Traditional weather forecasting relies on massive supercomputers that consume enormous amounts of energy and resources. Transitioning to energy-efficient AI models offers a sustainable way to predict extreme weather events with high precision. In this text-based course, you will explore how modern artificial intelligence transforms meteorological forecasting. You will understand the shift from computationally intensive numerical weather prediction to lightweight, accessible AI models that can run efficiently on standard hardware. What you'll learn: Understand the foundational concepts of traditional numerical weather prediction and why AI is a sustainable alternative; Explore modern AI architectures used in meteorology, including diffusion models and Graph Neural Networks; Apply parameter-efficient fine-tuning techniques like LoRA to adapt existing weather models to localized data; Analyze how GNSS-based sensing and satellite observations integrate with machine learning pipelines; Evaluate the energy consumption and carbon footprint of different forecasting methods. The training begins with key meteorological terminology and foundational machine learning concepts before guiding you through practical code implementations and architectural comparisons. You will study how these sustainable models are designed, trained, and evaluated using real-world datasets. This course is designed for beginners, environmental technology enthusiasts, and aspiring data scientists interested in sustainable AI, with no prior background in meteorology required. Start reading today to build a foundational understanding of green AI in weather forecasting.