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 demonstrates marked improvements in forecast skill, particularly in the Southern Hemisphere and tropics, achieving temporal and spatial accuracy that rivals or slightly surpasses that of 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.