FuXi Extreme

FuXi is a family of machine learning models designed to improve weather forecasting, particularly focusing on accuracy and efficiency compared to traditional numerical weather prediction (NWP) systems. Current research emphasizes developing FuXi models for various forecasting horizons (from subseasonal to extreme weather events), employing techniques like cascade architectures, variational autoencoders, and diffusion models to enhance forecast skill and address challenges like error accumulation and the underestimation of extreme events. These advancements have the potential to significantly improve the accuracy and lead time of weather forecasts, benefiting various sectors reliant on reliable weather predictions, such as agriculture, disaster management, and renewable energy.