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STC-ViT: Spatio Temporal Continuous Vision Transformer for Weather Forecasting

Operational weather forecasting system relies on computationally expensive physics-based models. Recently, transformer based models have shown remarkable potential in weather forecasting achieving state-of-the-art results. However, transformers are discrete and physics-agnostic models which limit their ability to learn the continuous spatio-temporal features of the dynamical weather system. We address this issue with STC-ViT, a Spatio-Temporal Continuous Vision Transformer for weather forecasting. STC-ViT incorporates the continuous time Neural ODE layers with multi-head attention mechanism to learn the continuous weather evolution over time. The attention mechanism is encoded as a differentiable function in the transformer architecture to model the complex weather dynamics. Further, we define a customised physics informed loss for STC-ViT which penalize the model's predictions for deviating away from physical laws. We evaluate STC-ViT against operational Numerical Weather Prediction (NWP) model and several deep learning based weather forecasting models. STC-ViT, trained on 1.5-degree 6-hourly data, demonstrates computational efficiency and competitive performance compared to state-of-the-art data-driven models trained on higher-resolution data for global forecasting.

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