MCT U-net: A Deep Learning Nowcasting Method Using Dual-polarization Radar Observations

In this study, we propose a deep learning nowcasting method called multivariate channel transformer U-net (MCT If-net). It incorporates the channel transformer into the basic U-net structure. Three dual-polarization radar variables, i.e., reflectivity ($Z$), specific differential phase ($K_{dp}$), and differential reflectivity ($Z_{dr}$), are used as inputs to the proposed model. Compared to traditional U-net, MCT U-net has two advantages: 1) We use channel transformer to replace the skip-connection operation to better capture global information. Due to the inability to fuse the semantic features at different scale, a simple skip connection operation cannot capture the global multi-scale context, and this study uses the self-attention mechanism in Transformer to tackle this problem. 2) MCT U-net extracts multi-scale features through down-sampling each variable and then concatenates and sends them to a channel transformer. The combination of multiple variables in higher semantic level helps the model learn each individual modality better. The experimental results show that MCT U-net improves nowcasting performance compared with traditional methods.