Graph neural networks have shown promising results in weather forecasting, which is critical for human activity such as agriculture planning and extreme weather preparation. However, most studies focus on finite and local areas for training, overlooking the influence of broader areas and limiting their ability to generalize effectively. Thus, in this work, we study global weather forecasting that is irregularly distributed and dynamically varying in practice, requiring the model to generalize to unobserved locations. To address such challenges, we propose a general Mesh Interpolation Graph Network (MIGN) that models the irregular weather station forecasting, consisting of two key designs: (1) learning spatially irregular data with regular mesh interpolation network to align the data; (2) leveraging parametric spherical harmonics location embedding to further enhance spatial generalization ability. Extensive experiments on an up-to-date observation dataset show that MIGN significantly outperforms existing data-driven models. Besides, we show that MIGN has spatial generalization ability, and is capable of generalizing to previous unseen stations.

翻译：图神经网络在天气预报领域已展现出有前景的结果，这对农业规划和极端天气准备等人类活动至关重要。然而，大多数研究专注于有限局部区域进行训练，忽视了更广阔区域的影响，限制了其有效泛化的能力。因此，在本工作中，我们研究实际中不规则分布且动态变化的全球天气预报，这要求模型能够泛化到未观测的位置。为应对此类挑战，我们提出了一种通用的网格插值图网络（MIGN），用于建模不规则气象站点的预报，其包含两个关键设计：（1）使用规则网格插值网络学习空间不规则数据以对齐数据；（2）利用参数化球谐函数位置嵌入进一步增强空间泛化能力。在最新观测数据集上的大量实验表明，MIGN显著优于现有的数据驱动模型。此外，我们证明MIGN具备空间泛化能力，能够泛化到先前未见过的站点。