A modified transformer model for the extended-range forecast of intraseasonal oscillation

Extended-range forecast has long maintained a difficult point for the seamless forecast system due to the lack of predictability, with intraseasonal oscillation (ISO), an important signal in many high-impact weather events, being an important source of that. To improve the accuracy of ISO extended-range forecast and make up the gaps in previous researches in this regard, a data-driven model ISOX is proposed for the intraseasonal components of atmospheric fields. Compared with the subseasonal forecast results from climate forecast system (CFS), and the climatological forecast, ISOX achieves higher accuracy for lead times longer than 13 days, with few spatial or temporal weak points. It also performed better in predicting the positive 2 m temperature ISO and lower tropospheric conditions in a heatwave event, surpassing CFS for lead times longer than 13 days. Finally, through gradient evaluation, the model is proved to be able to study the ISO signal movements of atmospheric systems. Thus, the success of this model may shed light on improving extended-range forecast skills and assist the timely detection and prevention of possible meteorological disasters.