Conventionally, Earth system (e.g., weather and climate) forecasting relies
on numerical simulation with complex physical models and are hence both
expensive in computation and demanding on domain expertise. With the explosive
growth of the spatiotemporal Earth observation data in the past decade,
data-driven models that apply Deep Learning (DL) are demonstrating impressive
potential for various Earth system forecasting tasks. The Transformer as an
emerging DL architecture, despite its broad success in other domains, has
limited adoption in this area. In this paper, we propose Earthformer, a
space-time Transformer for Earth system forecasting. Earthformer is based on a
generic, flexible and efficient space-time attention block, named Cuboid
Attention. The idea is to decompose the data into cuboids and apply
cuboid-level self-attention in parallel. These cuboids are further connected
with a collection of global vectors. We conduct experiments on the MovingMNIST
dataset and a newly proposed chaotic N-body MNIST dataset to verify the
effectiveness of cuboid attention and figure out the best design of
Earthformer. Experiments on two real-world benchmarks about precipitation
nowcasting and El Nino/Southern Oscillation (ENSO) forecasting show Earthformer
achieves state-of-the-art performance. Code is available:
this https URL .

该论文提出了一种名为 Earthformer 的深度学习模型，通过利用 cuboid attention 机制，成功地应用于 El Nino/Southern Oscillation (ENSO) 的预测，该方法表现出了最新的性能。