Reconstruct Radar Echo Radial Velocity Data based on Deep Learning

Improving the resolution of weather radar echoes enables forecasters to observe the occurrence, development and dissipation of severe weather in more detail, so as to improve the accurate early warning ability of severe weather, which is of great significance for reducing the impact of severe weather on people’s lives and work, as well as reducing its losses to the economy. Therefore, this paper proposes a new method, which is based on the Regularized Deep Residual Attention Network (R-DRAN), which reconstructs the radial velocity data of weather radar echoes with super-resolution without changing the hardware equipment, so as to assist in more accurate detection of weather processes. The results show that R-DRAN can not only capture more detailed information, but also effectively retain edge information, which significantly improves the reconstruction quality.