SCECA-Net: A Deep Learning-Based Model for Precipitation Nowcasting

In the context of precipitation nowcasting of severe convective weather, radar echo extrapolation is a commonly employed method. However, existing methods still face numerous challenges, such as inaccurate echo boundary predictions, redundant feature extraction, and prolonged inference time, which reduce efficiency. This article proposes an innovative spatial-channel enhanced convolutional attention network (SCECA-Net) model aimed at improving feature extraction and enhancing prediction accuracy. SCECA-Net adopts a convolutional neural network (CNN) architecture and incorporates SCECA modules [spatial and channel reconstruction convolution (SCConv) and efficient channel attention (ECA)], effectively reducing spatial and channel redundancies while increasing attention to critical echo regions and enhancing the extraction of temporal sequence features. Additionally, continuous convolutions in the Dense Layer further mitigate the risk of overfitting and reduce interference between features. The experimental results demonstrate that the proposed model exhibits outstanding performance in both efficiency and accuracy.