  Natural disasters caused by heavy rainfall often cost huge loss of life and
property. To avoid it, the task of precipitation nowcasting is imminent. To
solve the problem, increasingly deep learning methods are proposed to forecast
future radar echo images and then the predicted maps have converted the
distribution of rainfall. The prevailing spatiotemporal sequence prediction
methods apply ConvRNN structure which combines the Convolution and Recurrent
neural network. Although improvements based on ConvRNN achieve remarkable
success, these methods ignore capturing both local and global spatial features
simultaneously, which degrades the nowcasting in the region of heavy rainfall.
To address this issue, we proposed the Region Attention Block (RAB) and embed
it into ConvRNN to enhance the forecast in the area with strong rainfall.
Besides, the ConvRNN models are hard to memory longer history representations
with limited parameters. Considering it, we propose Recall Attention Mechanism
(RAM) to improve the prediction. By preserving longer temporal information, RAM
contributes to the forecasting, especially in the middle rainfall intensity.
The experiments show that the proposed model Region Attention Predictive
Network (RAP-Net) has outperformed the state-of-art method.

Machine Learning;Computer Vision and Pattern Recognition;Atmospheric and Oceanic Physics