Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting

2025-09-25

AI Summary

To address the challenge of poor generalization to unobserved regions in global weather forecasting—caused by irregular and dynamically varying spatial distributions of meteorological observation stations—this paper proposes the Mesh Interpolation Graph Network (MIGN). MIGN employs a learnable regular-mesh interpolation module to harmonize heterogeneous spatial sampling and incorporates parameterized spherical harmonic positional embeddings to explicitly encode spherical geometric priors and long-range spatial dependencies. Built upon a graph neural network architecture, MIGN uniformly processes dynamic, irregular meteorological fields. Evaluated on a state-of-the-art multi-source fused observational dataset, MIGN significantly outperforms mainstream data-driven models. Notably, it demonstrates strong spatial generalization in cross-station zero-shot transfer tasks. This work establishes a novel paradigm for high-accuracy, high-robustness global-scale weather forecasting.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Addresses global weather forecasting with irregularly distributed and dynamically varying data
• Overcomes limitations of local training by modeling broader spatial influences
• Enables generalization to unobserved weather stations through novel interpolation techniques

Innovation

• Mesh Interpolation Graph Network for irregular weather forecasting
• Regular mesh interpolation network aligns spatially irregular data
• Parametric spherical harmonics embedding enhances spatial generalization ability