MPFNet: Multiproduct Fusion Network for Radar Echo Extrapolation

Radar echo extrapolation (REE) plays a crucial role in convective nowcasting. Existing deep learning (DL)-based methods for REE are predominantly based on the analysis of echo composite reflectivity (CR). However, CR product solely offers single-layered echo intensity information, thereby losing vertical details of convective systems such as echo top heights, resulting in lower accuracy in REE. To address these limitations, this article proposes a multiproduct fusion network (i.e., MPFNet) for REE. First, residual convolutional encoders (RCEs) are designed, which adopt the ResNet to reuse features and combine attention mechanisms to improve focus on convective features. In addition, to leverage the correlations and complementarities among multiproduct features, a multiproduct fusion module (MPFM) that adopts multihead attention for modeling the interrelations among multiproduct features and depthwise separable convolution (DSC) for feature fusion is proposed. Finally, a residual decoder (RD) is designed instead of a conventional deconvolution decoder to aggregate fused features for the restoration of predicted echo sequences. The proposed MPFNet is verified by convective nowcasting experiments, and the experimental results demonstrate that it can effectively utilize multiple radar products for guiding REE. It significantly outperforms the state-of-the-art (SOTA) methods, such as Earthformer and PreDiff. Compared to the previously best-performing Earthformer, MPFNet achieves an average improvement of 1.3% and 2.4% in critical success index (CSI) and Heidke skill score (HSS), respectively, in convective nowcasting experiments on the SWAN dataset, and an average improvement of 1.2% and 3.2% in CSI and HSS on the MeteoNet dataset.