FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere
This addresses the need for a unified forecasting system for weather-climate services, offering improved predictions for global atmospheric conditions, though it builds incrementally on existing AI models.
The paper tackles the problem of seamless weather-to-subseasonal forecasting by proposing FengWu-W2S, a deep learning model that generates 6-hourly global atmosphere forecasts up to 42 days, enhancing predictive capabilities for variables like temperature and precipitation out to 3-6 weeks ahead.
Seamless forecasting that produces warning information at continuum timescales based on only one system is a long-standing pursuit for weather-climate service. While the rapid advancement of deep learning has induced revolutionary changes in classical forecasting field, current efforts are still focused on building separate AI models for weather and climate forecasts. To explore the seamless forecasting ability based on one AI model, we propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. FengWu-W2S can generate 6-hourly atmosphere forecasts extending up to 42 days through an autoregressive and seamless manner. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO). Moreover, our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential pathways for developing AI-based integrated system for seamless weather-climate forecasting in the future.