Fine-Tuning FuXi with CMA’s Reanalysis Data to Improve Forecasting

Artificial intelligence (AI) has emerged as a promising alternative to traditional numerical weather prediction (NWP) models. However, the mismatch between training and operational input data limits its practical application in real-time forecasting. This study overcomes this challenge by fine-tuning the FuXi model using the Global/Regional Assimilation and Prediction Enhanced System (GRAPES) reanalysis data, resulting in an adapted version termed FuXi-GRAPES (FuXi-G). FuXi-G was evaluated over the full calendar year 2021. Compared to its baseline configuration, FuXi-G delivers substantial improvements in forecast skill, particularly in the Southern Hemisphere and Tropics, with both temporal and spatial accuracy comparable to, or slightly exceeding, that of the National Centers for Environmental Prediction (NCEP) operational forecasts. The FuXi-G model also exhibited a notable reduction in error propagation, outperforming NCEP forecasts by day five and alleviating seasonal forecast biases in the Southern Hemisphere. A case study of Typhoon Khanun underscored the model's enhanced ability to predict high-impact weather events, including a critical directional shift of the typhoon associated with the breakdown of the subtropical high. These results suggest that fine-tuning AI models can substantially improve forecasting accuracy while avoiding the computational burden of retraining on new datasets, providing a scalable approach for implementing AI models in operational weather forecasting.