提出时空对齐注意力模型，提升短时降水预测精度。
STAA: Spatio-Temporal Alignment Attention for Short-Term Precipitation Forecasting
- 用时空对齐注意力模块解决多源数据不同步问题
- 在西南地区数据上使RMSE降低12.61%、极端降水预测更准
- 适合气象预报与防灾减灾领域研究者参考
短时降水精准预测对农业和灾害预防具有重要意义。近年来，基于多源数据的多模态模型虽提升了预测精度，但仍面临多源变量不同步、时空依赖建模能力弱及极端降水预测效果不佳等问题。为此，本文提出一种基于时空对齐注意力的短时降水预测模型，采用SATA作为时间对齐模块，STAU作为时空特征提取器，从降水信号中滤除高频成分并捕捉多尺度时间依赖。基于中国西南地区卫星与ERA5数据，该模型相较现有最优方法在RMSE上提升12.61%。
原文摘要 · Abstract (English)
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.
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