Global Weather

Global weather forecasting aims to accurately predict atmospheric conditions, crucial for numerous societal applications and scientific understanding. Current research heavily emphasizes developing efficient and accurate machine learning models, employing architectures like transformers, graph neural networks, and diffusion models, to improve upon traditional physics-based methods. These data-driven approaches focus on enhancing spatial and temporal resolution, quantifying prediction uncertainty, and reducing computational costs, leading to more precise and timely forecasts for various sectors including renewable energy and disaster preparedness. The resulting improvements in forecast accuracy and efficiency are significantly impacting both scientific understanding of the Earth's climate system and practical decision-making.