Spatio-Temporal Enhanced Contrastive and Contextual Learning for Weather Forecasting
Yongshun Gong, Tiantian He, Meng Chen, Bin Wang, Liqiang Nie, Yilong Yin
Abstract
Weather forecasting is of great importance for human life and various real-world fields, e.g., traffic prediction, agricultural production, and tourist industry. Existing methods can be roughly divided into two categories: theory-driven (e.g., numerical weather prediction (NWP)) and data-driven methods. Theory-driven methods require a complex simulation of the physical evolution process in the atmosphere model using supercomputers, while most data-driven methods learn the underlying laws from the historical weather records via deep learning models. However, some data-driven methods simply regard all weather variables of monitoring stations as a whole and fail to more granularly exploit complex correlations across different stations, while others prefer to construct large neural networks with massive learnable parameters. To alleviate these defects, we propose a spatio-temporal contrastive self-supervision method and a generative contextual self-supervised technique to capture spatial and temporal dependencies from the station-level and variable-level, respectively. Through these well-designed self-supervised tasks, uncomplicated networks obtain strong capability to capture latent representations for weather changes with time-varying. Thereafter, an effective encoder-decoder based fine-tuning framework is proposed, consisting of three self-supervised encoders. Extensive experiments conducted on four real-world weather condition datasets demonstrate that our method outperforms the state-of-the-art models and also empirically validates the feasibility of each self-supervised task.