STAA: Spatiotemporal 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 used multisource data as the multimodality input, thus improving the prediction accuracy. However, the prevailing methods usually suffer from the desynchronization of multisource variables, the insufficient capability of capturing spatiotemporal dependency, and unsatisfactory performance in predicting extreme precipitation events. To fix these problems, we propose a short-term precipitation forecasting model based on spatiotemporal alignment attention, with self-attention for temporal alignment (SATA) as the temporal alignment module and spatiotemporal attention unit (STAU) as the spatiotemporal feature extractor to filter high-pass features from precipitation signals and capture multiterm temporal dependencies. Based on satellite and ERA5 data from the southwestern region of China, our model achieves improvements of 12.61% in terms of root mean square error (RMSE), in comparison to the state-of-the-art methods.