一个模型实现全球大气从天气到亚季节的无缝预报
FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere
- 基于耦合海洋-大气-陆面结构与扰动策略，统一建模
- 可生成长达42天、每6小时更新的无缝预报结果
- 显著提升对气温、降水及MJO等信号的预测能力
实现基于单一系统在连续时间尺度上提供预警信息的无缝预报，是气象气候服务长期追求的目标。尽管深度学习已引发传统预报领域的革命性变革，但现有研究仍集中于为天气和气候预报分别构建AI模型。为探索单个AI模型实现无缝预报的潜力，本文提出FengWu-W2S，基于FengWu全球天气预报模型，引入海洋-大气-陆面耦合结构及多样化扰动策略。FengWu-W2S可采用自回归方式，实现长达42天、每6小时更新的全球大气无缝预报。历史回算结果显示，该模型在3至6周提前期内可靠预测大气状态，显著提升对全球地表气温、降水、位势高度以及马登-朱利安振荡（MJO）和北大西洋涛动（NAO）等季内信号的预测能力。此外，针对日到季节尺度预报误差增长的消融实验揭示了未来构建基于AI的集成式无缝气象气候预报系统的潜在路径。
原文摘要 · Abstract (English)
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.
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