Weather forecasting is a crucial task for meteorologic research, with direct
social and economic impacts. Recently, data-driven weather forecasting models
based on deep learning have shown great potential, achieving superior
performance compared with traditional numerical weather prediction methods.
However, these models often require massive training data and computational
resources. In this paper, we propose EWMoE, an effective model for accurate
global weather forecasting, which requires significantly less training data and
computational resources. Our model incorporates three key components to enhance
prediction accuracy: meteorology-specific embedding, a core Mixture-of-Experts
(MoE) layer, and two specific loss functions. We conduct our evaluation on the
ERA5 dataset using only two years of training data. Extensive experiments
demonstrate that EWMoE outperforms current models such as FourCastNet and
ClimaX at all forecast time, achieving competitive performance compared with
the state-of-the-art Pangu-Weather model in evaluation metrics such as Anomaly
Correlation Coefficient (ACC) and Root Mean Square Error (RMSE). Additionally,
ablation studies indicate that applying the MoE architecture to weather
forecasting offers significant advantages in improving accuracy and resource
efficiency.

在本文中，我们提出了 EWMoE 模型，它是一种能够准确预测全球天气的有效模型，它只需要较少的训练数据和计算资源，并利用 Meteorology-specific embedding、核心 Mixture-of-Experts (MoE) 层和两个特定的损失函数来提高预测准确性。通过对 ERA5 数据集进行了评估，并仅使用了两年的训练数据，我们进行了大量实验证明，EWMoE 在所有预测时间上均表现优于 FourCastNet 和 ClimaX 等当前模型，在异常相关系数（ACC）和均方根误差（RMSE）等评估指标上与最先进的 Pangu-Weather 模型具有竞争力。此外，消融实验结果表明，在天气预测中应用 MoE 架构可以显著提高准确性和资源效率。