VarteX: Enhancing Weather Forecast through Distributed Variable Representation
<|reference_start|>VarteX: Enhancing Weather Forecast through Distributed Variable Representation: Weather forecasting is essential for various human activities. Recent data-driven models have outperformed numerical weather prediction by utilizing deep learning in forecasting performance. However, challenges remain in efficiently handling multiple meteorological variables. This study proposes a new variable aggregation scheme and an efficient learning framework for that challenge. Experiments show that VarteX outperforms the conventional model in forecast performance, requiring significantly fewer parameters and resources. The effectiveness of learning through multiple aggregations and regional split training is demonstrated, enabling more efficient and accurate deep learning-based weather forecasting.<|reference_end|>
@article{ueyama2024vartex:,
    title={VarteX: Enhancing Weather Forecast through Distributed Variable
  Representation},
    author={Ayumu Ueyama, Kazuhiko Kawamoto, Hiroshi Kera},
    journal={arXiv preprint arXiv:2406.19615},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19615},
    primaryClass={cs.LG physics.ao-ph}
}