Convolutional neural networks (CNNs) have been utilized extensively to improve the resolution of weather radar. Most existing CNN-based super-resolution algorithms using PPI (Plan position indicator, which provides a maplike presentation in polar coordinates of range and angle) images plotted by radar data lead to the loss of some valid information by using image processing methods for super-resolution reconstruction. To solve this problem, a weather radar that echoes the super-resolution reconstruction algorithm—based on residual attention back-projection network (RABPN)—is proposed to improve the the radar base data resolution. RABPN consists of multiple Residual Attention Groups (RAGs) connected with long skip connections to form a deep network; each RAG is composed of some residual attention blocks (RABs) connected with short skip connections. The residual attention block mined the mutual relationship between low-resolution radar echoes and high-resolution radar echoes by adding a channel attention mechanism to the deep back-projection network (DBPN). Experimental results demonstrate that RABPN outperforms the algorithms compared in this paper in visual evaluation aspects and quantitative analysis, allowing a more refined radar echo structure, especially in terms of echo details and edge structure features.

The vertical structure of radar echo is crucial for understanding complex microphysical processes of clouds and precipitation, and for providing essential data support for the study of low-level wind shear and turbulence formation, evolution, and dissipation. Therefore, finding methods to improve the vertical data resolution of the existing radar network is crucial. Existing algorithms for improving image resolution usually focus on increasing the width and height of images. However, improving the vertical data resolution of weather radar requires a focus on improving the elevation angle resolution while maintaining distance resolution. To address this challenge, we propose a network for super-resolution reconstruction of weather radar echo vertical structures. The network is based on a multi-scale residual feedback network (MR-FBN) and uses new multi-scale feature residual blocks (MSRB) to effectively extract and utilize data features at different scales. The feedback network gradually generates the final high-resolution vertical structure data. In addition, we propose an elevation upsampling layer (EUL) specifically for this task, replacing the traditional image subpixel convolution layer. Experimental results show that the proposed method can effectively improve the elevation angle resolution of weather radar echo vertical structure data, providing valuable help for atmospheric detection.