Neural Weather Model

Neural weather models (NWMs) leverage machine learning to predict atmospheric conditions, offering a potentially faster and cheaper alternative to traditional numerical weather prediction (NWP) models. Current research focuses on improving accuracy and efficiency through techniques like parameter-efficient fine-tuning and the application of transformer and graph neural network architectures, often tailored for localized or specific weather phenomena prediction (e.g., severe convection, heat waves). These advancements are significant because they enable more timely and spatially-resolved forecasts, improving applications ranging from resource management and disaster preparedness to enhancing the reliability of communication networks.