Adaptive Spatio-Temporal Graphs with Self-Supervised Pretraining for Multi-Horizon Weather Forecasting

TL;DR

This paper introduces a self-supervised graph neural network framework that captures spatio-temporal weather patterns to improve multi-horizon forecasting accuracy and robustness across different datasets and locations.

Contribution

It presents a novel integration of GNNs with self-supervised pretraining and adaptation mechanisms for enhanced weather prediction performance.

Findings

Outperforms traditional NWP models and recent deep learning methods.

Effectively captures fine-grained meteorological patterns.

Demonstrates robustness across different datasets and locations.

Abstract

Accurate and robust weather forecasting remains a fundamental challenge due to the inherent spatio-temporal complexity of atmospheric systems. In this paper, we propose a novel self-supervised learning framework that leverages spatio-temporal structures to improve multi-variable weather prediction. The model integrates a graph neural network (GNN) for spatial reasoning, a self-supervised pretraining scheme for representation learning, and a spatio-temporal adaptation mechanism to enhance generalization across varying forecasting horizons. Extensive experiments on both ERA5 and MERRA-2 reanalysis datasets demonstrate that our approach achieves superior performance compared to traditional numerical weather prediction (NWP) models and recent deep learning methods. Quantitative evaluations and visual analyses in Beijing and Shanghai confirm the model's capability to capture fine-grained meteorological patterns. The proposed framework provides a scalable and label-efficient solution for future data-driven weather forecasting systems.