STAA: Spatio-Temporal Alignment Attention for Short-Term Precipitation Forecasting
<|reference_start|>STAA: Spatio-Temporal Alignment Attention for Short-Term Precipitation Forecasting: There is a great need to accurately predict short-term precipitation, which has socioeconomic effects such as agriculture and disaster prevention. Recently, the forecasting models have employed multi-source data as the multi-modality input, thus improving the prediction accuracy. However, the prevailing methods usually suffer from the desynchronization of multi-source variables, the insufficient capability of capturing spatio-temporal dependency, and unsatisfactory performance in predicting extreme precipitation events. To fix these problems, we propose a short-term precipitation forecasting model based on spatio-temporal alignment attention, with SATA as the temporal alignment module and STAU as the spatio-temporal feature extractor to filter high-pass features from precipitation signals and capture multi-term temporal dependencies. Based on satellite and ERA5 data from the southwestern region of China, our model achieves improvements of 12.61\% in terms of RMSE, in comparison with the state-of-the-art methods.<|reference_end|>
@article{chen2024staa:,
    title={STAA: Spatio-Temporal Alignment Attention for Short-Term Precipitation
  Forecasting},
    author={Min Chen, Hao Yang, Shaohan Li, Xiaolin Qin},
    journal={arXiv preprint arXiv:2409.06732},
    year={2024},
    doi={10.1109/LGRS.2024.3431099 10.1109/LGRS.2024.3431099
  10.1109/LGRS.2024.3431099},
    archivePrefix={arXiv},
    eprint={2409.06732},
    primaryClass={physics.ao-ph cs.LG}
}