Subseasonal forecasting, which is pivotal for agriculture, water resource
management, and early warning of disasters, faces challenges due to the chaotic
nature of the atmosphere. Recent advances in machine learning (ML) have
revolutionized weather forecasting by achieving competitive predictive skills
to numerical models. However, training such foundation models requires
thousands of GPU days, which causes substantial carbon emissions and limits
their broader applicability. Moreover, ML models tend to fool the pixel-wise
error scores by producing smoothed results which lack physical consistency and
meteorological meaning. To deal with the aforementioned problems, we propose a
teleconnection-informed transformer. Our architecture leverages the pretrained
Pangu model to achieve good initial weights and integrates a
teleconnection-informed temporal module to improve predictability in an
extended temporal range. Remarkably, by adjusting 1.1% of the Pangu model's
parameters, our method enhances predictability on four surface and five
upper-level atmospheric variables at a two-week lead time. Furthermore, the
teleconnection-filtered features improve the spatial granularity of outputs
significantly, indicating their potential physical consistency. Our research
underscores the importance of atmospheric and oceanic teleconnections in
driving future weather conditions. Besides, it presents a resource-efficient
pathway for researchers to leverage existing foundation models on versatile
downstream tasks.

借助预训练的 Pangu 模型和基于电涡流的时间模块，我们的研究提出了一种电涡流通知变压器方法，能够改善未来天气状况的预测能力并提高输出的空间精度。此外，我们的方法还提供了利用现有基础模型进行多功能下游任务的资源高效途径。