Fine-tuning a global weather model for superior subseasonal forecasting

Accurate subseasonal forecasting is socio-economically critical yet remains a great scientific challenge. Recent advances in machine-learning based global weather forecasting demonstrate superior skill on medium-range (1 to 15 days ahead) and subseasonal-range (15 to 42 days ahead) than the best traditional weather forecasting system. These data-driven models require immense computational resources for training, which are not widely available. Here we show, by using medium-range Graphcast model as pre-trained model and focusing on reducing iterative error accumulation, that fine-tuning is an efficient strategy to achieve impressive results for subseasonal forecasting. Our fine-tuned model GraphFT rapidly converges (trained on just three years of data), and significantly outperforms Graphcast and the leading deterministic traditional subseasonal forecasting system, even outperforming this system’s ensemble mean for key variables. Demonstrating the potential of fine-tuning for improving possibly both atmosphere and ocean forecasts with low computational costs and remarkable results.