Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling
<|reference_start|>Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling: Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting. However, most data-driven weather forecasting models are black-box systems that focus on learning data mapping rather than fine-grained physical evolution in the time dimension. Consequently, the limitations in the temporal scale of datasets prevent these models from forecasting at finer time scales. This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales beyond training dataset. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale (e.g., 300 seconds) and use a parallel neural networks with a learnable router for bias correction. Furthermore, we introduce a lead time-aware training framework to promote the generalization of the model at different lead times. The weight analysis of physics-AI modules indicates that physics conducts major evolution while AI performs corrections adaptively. Extensive experiments show that WeatherGFT trained on an hourly dataset, achieves state-of-the-art performance across multiple lead times and exhibits the capability to generalize 30-minute forecasts.<|reference_end|>
@article{xu2024generalizing,
    title={Generalizing Weather Forecast to Fine-grained Temporal Scales via
  Physics-AI Hybrid Modeling},
    author={Wanghan Xu, Fenghua Ling, Wenlong Zhang, Tao Han, Hao Chen, Wanli
  Ouyang, Lei Bai},
    journal={arXiv preprint arXiv:2405.13796},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13796},
    primaryClass={cs.LG cs.AI}
}