FuXi Weather-2: A unified neural paradigm for accurate global weather assimilation and forecasting

Currently, weather forecasting still relies primarily on Numerical Weather Prediction (NWP) models. While recent advances in machine learning (ML) have demonstrated the potential of ML-based forecasting models to revolutionize NWP, these models often struggle to accurately estimate the initial atmospheric states from raw observations and generate precise weather forecasts. To address this challenge, FuXi Weather introduces an innovative machine learning-based paradigm that integrates multi-source global observations to generate high-resolution analysis fields and medium-range forecasts. Notably, its performance across the vast majority of forecast targets is comparable to the ECMWF High-Resolution (HRES) model. This breakthrough signifies that AI-driven meteorological systems have evolved from experimental prototypes into mature, real-world solutions capable of competing with the most sophisticated traditional NWP frameworks.